diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
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--- /dev/null
+++ b/feature_crosses.ipynb
@@ -0,0 +1,1571 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "feature_crosses.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "ZTDHHM61NPTw",
+        "0i7vGo9PTaZl"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/feature_crosses.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Feature Crosses"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F7dke6skIK-k",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Improve a linear regression model with the addition of additional synthetic features (this is a continuation of the previous exercise)\n",
+        "  * Use an input function to convert pandas `DataFrame` objects to `Tensors` and invoke the input function in `fit()` and `predict()` operations\n",
+        "  * Use the FTRL optimization algorithm for model training\n",
+        "  * Create new synthetic features through one-hot encoding, binning, and feature crosses"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NS_fcQRd8B97",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4IdzD8IdIK-l",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, as we've done in previous exercises, let's define the input and create the data-loading code."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CsfdiLiDIK-n",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "10rhoflKIK-s",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ufplEkjN8KUp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1153
+        },
+        "outputId": "73da53f2-da42-482d-b4c3-5a7a9be1db83"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2645.2           539.2   \n",
+              "std         2.1        2.0                12.6       2185.4           419.0   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1464.8           299.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3153.0           647.0   \n",
+              "max        42.0     -114.5                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1431.6       501.2            3.9               2.0  \n",
+              "std        1165.4       382.9            1.9               1.2  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         791.0       283.0            2.6               1.5  \n",
+              "50%        1169.0       410.0            3.5               1.9  \n",
+              "75%        1722.2       605.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2645.2</td>\n",
+              "      <td>539.2</td>\n",
+              "      <td>1431.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2185.4</td>\n",
+              "      <td>419.0</td>\n",
+              "      <td>1165.4</td>\n",
+              "      <td>382.9</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1464.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>791.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1169.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3153.0</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1722.2</td>\n",
+              "      <td>605.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2639.9           540.0   \n",
+              "std         2.1        2.0                12.5       2167.1           427.5   \n",
+              "min        32.6     -124.3                 2.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1450.8           293.0   \n",
+              "50%        34.3     -118.6                29.0       2127.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3150.0           652.0   \n",
+              "max        41.8     -114.3                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1424.7       501.3            3.9               2.0  \n",
+              "std        1104.7       388.3            1.9               1.0  \n",
+              "min           6.0         2.0            0.5               0.1  \n",
+              "25%         785.8       278.0            2.6               1.5  \n",
+              "50%        1158.0       406.0            3.5               1.9  \n",
+              "75%        1718.0       606.0            4.7               2.3  \n",
+              "max       15507.0      5050.0           15.0              26.5  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2639.9</td>\n",
+              "      <td>540.0</td>\n",
+              "      <td>1424.7</td>\n",
+              "      <td>501.3</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2167.1</td>\n",
+              "      <td>427.5</td>\n",
+              "      <td>1104.7</td>\n",
+              "      <td>388.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1450.8</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>785.8</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.6</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1158.0</td>\n",
+              "      <td>406.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3150.0</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1718.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.8</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>15507.0</td>\n",
+              "      <td>5050.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>26.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.7\n",
+              "std                 115.9\n",
+              "min                  15.0\n",
+              "25%                 119.8\n",
+              "50%                 181.3\n",
+              "75%                 265.2\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                206.3\n",
+              "std                 116.3\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 177.9\n",
+              "75%                 264.4\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>177.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oJlrB4rJ_2Ma",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NBxoAfp2AcB6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hweDyy31LBsV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## FTRL Optimization Algorithm\n",
+        "\n",
+        "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S0SBf1X1IK_O",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "1Cdr02tLIK_Q",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "131a5392-689f-4f86-83be-44b056fc00dc"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(training_examples),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 168.56\n",
+            "  period 01 : 117.52\n",
+            "  period 02 : 177.62\n",
+            "  period 03 : 168.49\n",
+            "  period 04 : 122.88\n",
+            "  period 05 : 133.67\n",
+            "  period 06 : 143.88\n",
+            "  period 07 : 148.24\n",
+            "  period 08 : 128.84\n",
+            "  period 09 : 130.29\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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H+dfH+6l3yTJrIYTwF0lgREDx9EBScailxIfEotPofHauqOBI7hpxC33D+rCj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\nCBFzJMAIIYQQIub8P1KweDI7TcTCAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i4lGvqajDWlw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## One-Hot Encoding for Discrete Features\n",
+        "\n",
+        "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n",
+        "\n",
+        "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n",
+        "\n",
+        "| # | feature_value |\n",
+        "|---|---------------|\n",
+        "| 0 |             2 |\n",
+        "| 1 |             0 |\n",
+        "| 2 |             1 |\n",
+        "\n",
+        "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n",
+        "\n",
+        "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n",
+        "|---|-----------------|-----------------|-----------------|\n",
+        "| 0 |             0.0 |             0.0 |             1.0 |\n",
+        "| 1 |             1.0 |             0.0 |             0.0 |\n",
+        "| 2 |             0.0 |             1.0 |             0.0 |"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KnssXowblKm7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Bucketized (Binned) Features\n",
+        "\n",
+        "Bucketization is also known as binning.\n",
+        "\n",
+        "We can bucketize `population` into the following 3 buckets (for instance):\n",
+        "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n",
+        "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n",
+        "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n",
+        "\n",
+        "Given the preceding bucket definitions, the following `population` vector:\n",
+        "\n",
+        "    [[10001], [42004], [2500], [18000]]\n",
+        "\n",
+        "becomes the following bucketized feature vector:\n",
+        "\n",
+        "    [[1], [2], [0], [1]]\n",
+        "\n",
+        "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n",
+        "\n",
+        "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cc9qZrtRy-ED",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_boundaries(feature_values, num_buckets):\n",
+        "  boundaries = np.arange(1.0, num_buckets) / num_buckets\n",
+        "  quantiles = feature_values.quantile(boundaries)\n",
+        "  return [quantiles[q] for q in quantiles.keys()]\n",
+        "\n",
+        "# Divide households into 7 buckets.\n",
+        "households = tf.feature_column.numeric_column(\"households\")\n",
+        "bucketized_households = tf.feature_column.bucketized_column(\n",
+        "  households, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"households\"], 7))\n",
+        "\n",
+        "# Divide longitude into 10 buckets.\n",
+        "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "  longitude, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"longitude\"], 10))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U-pQDAa0MeN3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train the Model on Bucketized Feature Columns\n",
+        "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n",
+        "\n",
+        "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YFXV9lyMLedy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0FfUytOTNJhL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "02a32333-f095-449e-d86f-6e25963094e4"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.78\n",
+            "  period 01 : 143.41\n",
+            "  period 02 : 126.92\n",
+            "  period 03 : 115.77\n",
+            "  period 04 : 108.02\n",
+            "  period 05 : 102.28\n",
+            "  period 06 : 97.88\n",
+            "  period 07 : 94.50\n",
+            "  period 08 : 91.68\n",
+            "  period 09 : 89.27\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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4IBt/Os6J9Fx3RxIREanU1MBUgHo16tKlTgdKvLMxB55g8YbD7o4kIiJSqamB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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZTDHHM61NPTw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JQHnUhL_NRwA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ro5civQ3Ngh_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RNgfYk6OO8Sy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "AFJ1qoZPlQcs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Feature Crosses\n",
+        "\n",
+        "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n",
+        "\n",
+        "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n",
+        "\n",
+        "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-Rk0c1oTYaVH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train the Model Using Feature Crosses\n",
+        "\n",
+        "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n",
+        "\n",
+        "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-eYiVEGeYhUi",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xZuZMp3EShkM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "5d0015d0-1139-425d-f48d-2ab532d33d64"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.69\n",
+            "  period 01 : 135.53\n",
+            "  period 02 : 118.46\n",
+            "  period 03 : 107.30\n",
+            "  period 04 : 99.62\n",
+            "  period 05 : 93.90\n",
+            "  period 06 : 89.67\n",
+            "  period 07 : 86.24\n",
+            "  period 08 : 83.49\n",
+            "  period 09 : 81.18\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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YJxDoHkDvRj2wuReSau4lbm+61ZFEREScmgqMkxjapD8Ow4FLowMsWJdAeblm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\nkYLzf6uMwVPOSCUqAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0i7vGo9PTaZl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3tAWu8qSTe2v",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-_vvNYIyTtPC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ymlHJ-vrhLZw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Try Out More Synthetic Features\n",
+        "\n",
+        "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/feature_sets.ipynb b/feature_sets.ipynb
new file mode 100644
index 0000000..90b92b3
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1512 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "feature_sets.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "IGINhMIJ5Wyt",
+        "pZa8miwu6_tQ"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/feature_sets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zbIgBK-oXHO7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Feature Sets"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bL04rAQwH3pH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F8Hci6tAH3pH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F5ZjVwK_qOyR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As before, let's load and prepare the California housing data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SrOYRILAH3pJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dGnXo7flH3pM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jLXC8y4AqsIy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1153
+        },
+        "outputId": "8cd49b03-4415-40ad-b905-6ac28657f0c7"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.5                28.6       2613.4           534.4   \n",
+              "std         2.1        2.0                12.5       2117.3           411.6   \n",
+              "min        32.5     -124.3                 2.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1455.8           295.0   \n",
+              "50%        34.2     -118.5                29.0       2114.5           433.0   \n",
+              "75%        37.7     -118.0                37.0       3127.0           644.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1417.3       495.9            3.9               2.0  \n",
+              "std        1136.5       374.4            1.9               1.2  \n",
+              "min           3.0         1.0            0.5               0.1  \n",
+              "25%         787.0       280.0            2.5               1.5  \n",
+              "50%        1165.0       407.0            3.5               1.9  \n",
+              "75%        1714.0       600.0            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2613.4</td>\n",
+              "      <td>534.4</td>\n",
+              "      <td>1417.3</td>\n",
+              "      <td>495.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2117.3</td>\n",
+              "      <td>411.6</td>\n",
+              "      <td>1136.5</td>\n",
+              "      <td>374.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1455.8</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>787.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2114.5</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1165.0</td>\n",
+              "      <td>407.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3127.0</td>\n",
+              "      <td>644.0</td>\n",
+              "      <td>1714.0</td>\n",
+              "      <td>600.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.7     -119.6                28.6       2716.3           551.4   \n",
+              "std         2.1        2.0                12.7       2322.0           444.2   \n",
+              "min        32.5     -124.2                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1476.0           299.0   \n",
+              "50%        34.3     -118.5                29.0       2155.5           435.0   \n",
+              "75%        37.7     -118.0                37.0       3200.2           659.0   \n",
+              "max        42.0     -114.5                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1459.1       514.0            3.9               2.0  \n",
+              "std        1174.3       407.4            1.9               1.2  \n",
+              "min           6.0         2.0            0.5               0.0  \n",
+              "25%         796.0       284.8            2.6               1.5  \n",
+              "50%        1173.5       414.5            3.6               2.0  \n",
+              "75%        1739.0       614.0            4.8               2.3  \n",
+              "max       15507.0      5050.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2716.3</td>\n",
+              "      <td>551.4</td>\n",
+              "      <td>1459.1</td>\n",
+              "      <td>514.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.7</td>\n",
+              "      <td>2322.0</td>\n",
+              "      <td>444.2</td>\n",
+              "      <td>1174.3</td>\n",
+              "      <td>407.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1476.0</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>796.0</td>\n",
+              "      <td>284.8</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2155.5</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1173.5</td>\n",
+              "      <td>414.5</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3200.2</td>\n",
+              "      <td>659.0</td>\n",
+              "      <td>1739.0</td>\n",
+              "      <td>614.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>15507.0</td>\n",
+              "      <td>5050.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                206.4\n",
+              "std                 115.9\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 179.3\n",
+              "75%                 264.1\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                209.6\n",
+              "std                 116.1\n",
+              "min                  15.0\n",
+              "25%                 122.0\n",
+              "50%                 182.5\n",
+              "75%                 267.0\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>209.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>122.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>182.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>267.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hLvmkugKLany",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Develop a Good Feature Set\n",
+        "\n",
+        "**What's the best performance you can get with just 2 or 3 features?**\n",
+        "\n",
+        "A **correlation matrix** shows pairwise correlations, both for each feature compared to the target and for each feature compared to other features.\n",
+        "\n",
+        "Here, correlation is defined as the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient).  You don't have to understand the mathematical details for this exercise.\n",
+        "\n",
+        "Correlation values have the following meanings:\n",
+        "\n",
+        "  * `-1.0`: perfect negative correlation\n",
+        "  * `0.0`: no correlation\n",
+        "  * `1.0`: perfect positive correlation"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 343
+        },
+        "outputId": "80be8076-3586-4e57-b041-83d1090ca019"
+      },
+      "cell_type": "code",
+      "source": [
+        "correlation_dataframe = training_examples.copy()\n",
+        "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n",
+        "\n",
+        "correlation_dataframe.corr()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>-0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    latitude  longitude  housing_median_age  total_rooms  \\\n",
+              "latitude                 1.0       -0.9                 0.0         -0.0   \n",
+              "longitude               -0.9        1.0                -0.1          0.0   \n",
+              "housing_median_age       0.0       -0.1                 1.0         -0.4   \n",
+              "total_rooms             -0.0        0.0                -0.4          1.0   \n",
+              "total_bedrooms          -0.1        0.1                -0.3          0.9   \n",
+              "population              -0.1        0.1                -0.3          0.8   \n",
+              "households              -0.1        0.1                -0.3          0.9   \n",
+              "median_income           -0.1       -0.0                -0.1          0.2   \n",
+              "rooms_per_person         0.1       -0.1                -0.1          0.1   \n",
+              "target                  -0.2       -0.0                 0.1          0.1   \n",
+              "\n",
+              "                    total_bedrooms  population  households  median_income  \\\n",
+              "latitude                      -0.1        -0.1        -0.1           -0.1   \n",
+              "longitude                      0.1         0.1         0.1           -0.0   \n",
+              "housing_median_age            -0.3        -0.3        -0.3           -0.1   \n",
+              "total_rooms                    0.9         0.8         0.9            0.2   \n",
+              "total_bedrooms                 1.0         0.9         1.0           -0.0   \n",
+              "population                     0.9         1.0         0.9           -0.0   \n",
+              "households                     1.0         0.9         1.0           -0.0   \n",
+              "median_income                 -0.0        -0.0        -0.0            1.0   \n",
+              "rooms_per_person               0.0        -0.1        -0.0            0.2   \n",
+              "target                         0.0        -0.0         0.1            0.7   \n",
+              "\n",
+              "                    rooms_per_person  target  \n",
+              "latitude                         0.1    -0.2  \n",
+              "longitude                       -0.1    -0.0  \n",
+              "housing_median_age              -0.1     0.1  \n",
+              "total_rooms                      0.1     0.1  \n",
+              "total_bedrooms                   0.0     0.0  \n",
+              "population                      -0.1    -0.0  \n",
+              "households                      -0.0     0.1  \n",
+              "median_income                    0.2     0.7  \n",
+              "rooms_per_person                 1.0     0.2  \n",
+              "target                           0.2     1.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RQpktkNpia2P",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n",
+        "\n",
+        "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n",
+        "\n",
+        "Use this information to try removing features.  You can also try developing additional synthetic features, such as ratios of two raw features.\n",
+        "\n",
+        "For convenience, we've included the training code from the previous exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bjR5jWpFr2xs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jsvKHzRciH9T",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        "    \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "\n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g3kjQV9WH3pb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "varLu7RNH3pf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DSgUxRIlH3pg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "200e0132-1dca-4373-d0e2-a291e15e21ec"
+      },
+      "cell_type": "code",
+      "source": [
+        "minimal_features = [\n",
+        "  \"median_income\",\n",
+        "  \"latitude\",\n",
+        "]\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 164.95\n",
+            "  period 01 : 125.49\n",
+            "  period 02 : 116.83\n",
+            "  period 03 : 116.10\n",
+            "  period 04 : 116.29\n",
+            "  period 05 : 115.16\n",
+            "  period 06 : 114.18\n",
+            "  period 07 : 113.66\n",
+            "  period 08 : 113.12\n",
+            "  period 09 : 112.28\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RVxnmqkRERLo3BZijFO+KJCU+im9zPWTENfTBbPfsDHNVIiIi3ZsCTDvITHdT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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IGINhMIJ5Wyt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BAGoXFPZ5ZE3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "minimal_features = [\n",
+        "  \"median_income\",\n",
+        "  \"latitude\",\n",
+        "]\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RidI9YhKOiY2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Make Better Use of Latitude\n",
+        "\n",
+        "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n",
+        "\n",
+        "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hfGUKj2IR_F1",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "f8ce3273-76a7-4ee7-d16e-e5d3b0e50980"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f82b7eccfd0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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5a4GNc3jrhHjlfL6sb1+ELeuW4oOP/Xlt5w9v/UnVwo0s/ghGRzZk/Xd/93f4\nq7/6K7zyyisAgEgkApqmAQC1tbXwer0YGxuD2z1d3OF2u+H1ylc4u1x2WCyFCzldGJuSbEMyAfC4\nKnD9ikX4s81X4oU/fIgjJy/A649IComsbqtHU0ON6OvO6gp4XBUY9Qu3dPhD6QfJHZ+4AptvvBx/\neOtPyr5QAaDMJnSuaIBnT7/g8TK0BVcsdsFeQc967cLYFHwiWsb+YBQUbYWnrlL3Yy42Ho9T1fu/\neXc77BU0jpy8gLGJCOpq0tfYlz9zNeJcUnTUp7uKwU8fujlvIZY/XQhkxGgKQbWjArSN1kXHundw\nHF+7swI2WvpRNBli0SWy+FO6jWyisQT8ARauKkbV58oFtdcsQRmFPq+SV+Irr7yCNWvWYPFi4V5U\nsfFpSseq+f1hRe/TSiQcg9kkrJBlMgEPb2/H5QurwFgpTE5GsOWmy/HpaxdjxDeFv/1Nl2A4zkZT\n+Py6K+D1Sme/VjXXyuav3+o5jye+dA32vndWc+gvX7hkCs+80gtapDgnwibwzCsnBPPIXJyD2yne\ndsPF4rLnqdTxeJyavgN/LWXnOn2+KYz6w/CKLNQmgiyGzk8glodMKZdM4rd/+FDz55VwuOc8bmlv\ngNNuRTCcn3Tr2EQEg38aF5Vm5WsUjp3yiob35bYhtD0j1zxovWYJ0uh1XqWMuqRBPnDgAM6dO4cD\nBw5gZGQENE3DbrcjGo3CZrPh4sWLqK+vR319PcbGplWARkdHsWbNmrwPPF+kCqdSKeDf/78PsHb5\nzJuRsVI4fGJE1EB+YtUi2Bn5FTUfcjx6alS0hcoXZPH8H0/NmTHmOSgzpIIXQgEww8BIFc6Rlqnp\nvG42WodCKGXXvgEc+UBboZVSfIEoImwC7W11kqIySpD7zkrEfdSct52v980Q41HT900gFBpJy/JP\n//RPmX//y7/8CxobG9Hd3Y3du3fjc5/7HF577TWsW7cOq1evxo4dOxAIBEBRFLq6uvDoo48W/ODl\nqHYwcDtp0cIWPnQMTN+MUnlRG01hy7qlivbNV79+5sbL8YPnxIt3uvv0lTMsBP5gFL/efRqnz/pn\neRX8wqO7bwz+YFTTlKz5hJ6LmNxqY6ViOCaIjxtVQrWDRrWDwd0bWvI2yMuXiKd+lH4fJectXRDW\nj4PHhRefQup7BEKxUZ08+da3voXvfe972LVrFxoaGrBlyxZYrVY89NBD+MpXvgKTyYRvfOMbcDrn\nPofBWCl0LKuXXWFn34xSBVmxOIdQOKbIQ+Zx2mmsXV6Y9qtiQVspvHVyWiIx16sQarshiLP15qU4\nfXYCw94Qkqm0tnmjx4GtNysehi5uAAAgAElEQVRb7ImFXTe0NyoSw8m32651cbrjwjepTb7VBICh\nKZhMwOGTIzh11i8YNpYT96lx0OhcXq9o8bdr3wD2dw2Lvk6mmhFKAcWW5Vvf+lbm37/85S9nvX77\n7bfj9ttv1+eodERJ6Dj7ZixESHHbxhaEo4kZRq28EH6EZy9khMKzBGFeOnBmxtSlZCo9oemlA2cU\nhU3F5DY5LqlpBrhajn7oxZnhI2htEvdupei80oP3Ppz2fMXCxpL3ooPBD758DZz22cWGuSjxtMlU\nM0IpYIwqBgn40PEjX+yA1SL8dbNvRjVyhGqO4b7blumqHOZyWEFbCyv1ZaPNuGnFQtGKXaVqUoRp\n8m0Vk/p876APq1oK30rHtxwd+eAitNRBnfpYWKMg9/tL3Ytrl3sUGWNAmYwuqXkglALGq/fPgQ/v\nvdl7AXGRsYbZIwa5ZBKpVAo2msooV9loCjeuXJhXXpSxUrAzFvigjwELRhJIcPpJfTXVVyISTWA8\nwGYq0ytoCrTVjNoCFiHNN9QoeWn5/Ka1TaDMpkxOn7ZSSCVTYBWM9NSCxWxGTKUwh1hlttD3V1Kj\nIKfcJeVpm03A+jUNpOaBUBIY3iArqdLsGRwHtacP2za2YNe+Aew9NjPXlC15qQW+oGTYO6Xp80Lo\naYwBIBLlcPUVLrzRM5KpTPeH4tjffR6L6x2CDzPiVagn35SI3OfdVbZZOf1YnMMTz72r6/hFHrFF\nrhaEvr+YNCgg38KUbajFCunWtzfivk8t0+07EAj5YGiDrLRK08fn4JIp9A4IVz3nU4UpV1BSCviC\nUfQO+gRfm4rEsaGjEb0D46SSOk8YK4VVzbUzWm94lCxwlFZpZ+f0GSuFzuXyxY1acFUxqqeqiSH1\n/YVqFMRy6clUCmaTaYahXt1ah1vWNuJ4P7mGCaWLoQ2y2hGMx/vGRNuTtFZhapnLPBc47VZRD2oi\nxOK2axbj7g0tmAyxqGAsiLDpkDll+CoE/eA9Ol6pi08NuJ0MOpZ5FBsHLa1m2Z/xBaMwQVgwRy2r\nmmtxqOc8uDwcZTXV0jxS99VbJ0ZmDEoZD7DYd2wYmzqb8NSD15FuAELJYmiDLBXeE2JiikWNgxY0\nTFrzpVrmMs8FgSlxxSXaSsFhp2GhTNhzbMjQKkeFJNej4w3i6tY6VaIUYmFcNs5hfDKcuU6zX8v+\nTP/QBH6yq0eX7xSLcchntoOaaulspO6rbGOcDR/lIt0AhFLF0AZZ7QhGt9OGVc1uzeFEIdQuCkqR\naIzDK4fOAIBgiBAgKkdySFZHD4yD3SA+zlMMPozLT0HiF0q01QyTCYjGkqjNWTQxVgrHTusXsTl9\nbgK01axZbU5NtXQ2Wu4r0mtMKHUM79Zs29iCDe0NMCvoEGpvq8P2W9syA99NSIfTNrRrq8Lki0pW\nNdeqP/ASo+u0l0x2ygMl1dVaefHSqEV+ChIbT2Za1bInInHJJH69+xQO9UhLparBF2QR02CM3U4G\nn123VHMOV6olykYLL2xIVwCh1DG0hwzwPcDLAZNJtLCqtmo6B0eZzdi2sQVcMoXjfWOYCLHoHRxH\nLH4K99zapkilS6j6s6HOjvNjhR2mUUikJvsQz0MeKY+uqpJGhQr1t2yC4Rje7JGXr+zuGwOXTAlG\nf/KBtmjzjv/i7tVov2oRvN6gbNuSGGK59FQqNatTAiBdAYTSx/AGmefO9UvBxjic+tiPiRAL16Xw\n9KbOxXBX2WbcqLlV0eMBFodPjuBY3yg+sapBNmcqVP1Z7ricDEwm4e9CPA95pNInE6EYnnz+PVX5\neH7R996HFxX1GPsCURwvgG66lrYnsxnw1FSA42aG2tXWJIjl0rlkEiaTieirE8oOwxtkIW/1hqsX\ninq7Urm+aCyJPUeHkEql8MVbhXsXpT4vNgqyHFjTVof+c5OkHzkPsj268cBMHWi1+Xgl/fXZVDto\nTBRAVU3L9ZxMpnXhn/s/76uuSRDypnNboqR6lwmEUsbwOWT+wcXn13hv95VDZ8DGOYz6wzPyn0qq\notPjGYVzplKfLxdjbDalZTNNSIfzN3U2wQTM0F/mWVzvIJ4HIHgt5cIbiscf6IRLJKKQr3ymGO2t\ndbpKt+ZL37kJHDkpHGoXOgd84dqOZ47g+784gh3PHMHOPX3gJEq8eUNNjDGhXDC0hyz14Hqz9wK6\nTo/CH4zNCJUpqd6Mxjh4JyJo8jhmvSb1ecZqvnRcczv/mEfMY0+mgBuuXojbrl2SCUXveOaI4DbC\n0fndj6xl4H2ETYh6q/nKZ+aSXWVtNvcL5lbngmA4hlF/RPA1oXMgJgISiSZw723LCm50tea5CQQ1\nGNogy/Uq8v2KuaEyRa1SqdmWLFNV3VInWEBWKoaYJwWgyk4jEJ7dd9076MOWdUsxGWIRSyTz0l82\nMmKGAhAPuxZSPjOXVc212L6pDZFYHAe69TfGjMWsSSf792+cEX0t9xxILawPnxzBhx/70LGsviD9\n8FoWXASCVgxtkNX2Knad9uLO9c3YtrEFpz72Y0hEe9pGU/BkGaDcm9blpLG43oFwNA5/MC02EorG\nEYuXVszaZIKgMQaA8UAUP3juvUsFcDSYrGEb2czngi65yU1iUqtK5S/FUNNf3zvoAxvn8PD/81Ze\nalpi3LByIQ5oqNwORhKir+WeA7mIgC8YK1g/vJYFF4GgFUMv8RgrhTWtysfR+YIsJkMsElwKEVb8\ngXHtVZ5ZVdnZeWpfMIZzoyGsWOrGDVcvRDKFkjPGQLq4hg+jC+EPTX8fMfWj+VzQlU9v8baNLZl+\nd7NpOlevRj4zu19eDF8giv5zE5iK6tsn7nLQ2NTZhM/edLlu23Q7GcFzwC+s5dC7Hz7fUZkEgloM\n7SED6bCsUswmoIKxyK7Io+z0jSh1077zwaioISsVBCLvothoCpU2C/xBlrSSIL/Qc76VwNmf9/rD\n+OlLvYLHkQLw76++r3i7SjADuPIKFza0N2JwOKDLNk2mdG+yUF2G0oiA3umTfEdlEghqMbRBZuMc\nevqV914mU+mCG7lQ93sfelFZcRrbN7Vq0tQtJWKJJK6/qh7vfjgqWwUei3N49N4O0FaKFLcg/9Az\nv418HuqMlUJTvRMrmt042C1ctRyKikd7tJAE8NaJi3jrxEVUVVp12abbaYOnpkL09emWMa/ofal3\n+iTfXD+BoBZDh6zVDnaorWIyhmb5Epfo+1IA9ncNY9e+AcXhtFKltsqGzTdcrqgly+W0weOyk1aS\nLPINPecLL4f59smRouwvF6mhJGqQW8DwEYGnHrweN65YqGkbapGS55zPqRpC4TC0h6y2qKu9bTo3\nfM+tbTjWN5rRBBaCL9wR85JsIoVQpUR7W7o/1QT58D55CM1mLkUouGQSTz5/VLA/vFzIlq1VAmOl\n8KXNy2G3WWYoca1qdmNDeyPYuPohHVJoGXVJIGjF0AZZKqSYroJOiN5kdsaCT6xqkMxb8XkktZq6\npUSC47Brb5+kMXY5aaxdpm5e7Xwj39CzFnbu6S9rY7yxczHuWr9UtQHNXgT5AlHsOTaE3oExHOg+\nr3tbElH9IhQTQxtkYHqF23XaC1+QRU0ljfZlHmzf1IoEl5K8ybZtbEE8weHgceHcHJ9HErtpw2wC\nh09ckPSy55oD3RdgtUg/uC5bUFW0Fg8iwCANf34qGEtBtKmLyYmBMdy1fqnmzzNWCvu7h2fpzhei\nLWkuFlyE+YfhDTIApFIpTEXTua6JqRjePnkBJhNwzy2tkjcZZTbDLLHKzg3h5t60oXAMbAkbYx65\nAQFnR4O6hwJzIQIM0uSen7Q2tXAPuRg3Xr0Ap89NztLRniu8ExH8evdpfGnzctW/MRvncO5iAG8c\nF+6B7u4bw2duvDxTpEkWd4RywPAGede+gVlh42gsiX3HhmE2mURX0ZnZsb3CxTI2msKWdVdI7rva\nwYgKapQTE5f6swvpIRABBmlyz49aY1xbZcN9ty8HAHzrHw9Cg7hWQXjr5AjsNovi35hLJvG7vf14\nSybyNB6I4onn3sVkKEYWd4SywdBXp5wI/7FTo4LN/XyxzBs9I6J9urE4h1BYSYVp6QmCqMXlZAra\n4kEEGKTRMkwiFz6a45uMlIwx5nmz9wLCEkI82ezaN4B9x4YVpYEmQrHMQJk9R4ewa99AnkdKIBQW\nQxtkubYnfyiG3+w+PWtijJJiGavFLGukJkNsSeePlTIVjePlg4OSk3XyIR/FKyMgNylK7jp2OdKz\nql0OBo2eStCWae0uG01h49pGbNvYAi6ZxDP/+YHux58v0RiH373eJ/s+Ns6h6/So5v2QxR2h1DF0\nyFpJ29PhkyOoyAqZsXEO3aflvZGEAmHgageDWhVtV6UKPwcaKEz4eL4KMCjJm7NxDrE4J3p+aqts\nePyBTkTYBHa/exb7c3SlozEOZpMJlNmMnXv68KcLpVmVfeqsX7ZOYTLEwhdUF6rPhqhrEUodQ3vI\nUo392fArZy6ZxG92n8bElPxNzyUBrz+sy/7LhUJ5GIyVwmoRzfHVrbWGLcgRmtXNh1az5/8+8dx7\nmaLEXJYvqckop/UOjgu+p7tvDMFwDMdOXSzgt8kP/6U6BaloQbWDgdtJa94Hv7hTMruaQJgLDO0h\nc8kkkqkUGKtZcvQhv3Lec2wIh9UoHpmkZP3TbNvYgtNnJ8q6X5SnkB6G2JmUP8PliVzenOOSM7xd\nPvVhoynE4hxoKwUghcMnR3DqrB/LlrhEw9rjgSg+vhiEP6SPqlYhcDkZ7H73LHoHx0WjBbE4h8sX\nVsEX1Nbutbq1Fi8fHCSV/ISSxdAGmS8AkYO2UqCtlKrCGbMZktq7PAkuhbCId1NuFCp8zMY5HBfR\nHD/eP4b1qxvgMZhcp1Re2BeMolvkfNgZC9a01OLIB9O51PEAi7dOjkguPN/5YARmExRJpM4Fdpt1\nxgIku8p+681L8aNfdWHYG9J0/LWXDG8qlSKV/ISSxrAGWU1lajTG4eUDg6p0r62UshW1Wj3tUkZv\n6Uxe5CKWSEp4dywef+491FYxWNVSh01rm+CuspW9cZbKm9dUMvCLFLL5QyxOnZ0QfE0qCvTeKW9J\nGmP+d+3pF48WiM0m5+VeacqEGCf85UwAvr11FTwuO3Y8c0R0H2KzqwmEYmJYg6zWEH7wsR9WiRs7\nl3giqSh8q1ZPu1S5acVCTdKZQspbucVMLict2689HmCxvyutylRrgFAjP6tbSFp1dWstTgyOqzbW\nUsQkjPVc0eipxPfvXYtQOIYDXcKRrPFAFGaRvAV/p0rdsy4nA4/LTkYpEsqC8nyaKUDtFCZ/kFVs\njAHp8G120YgRCrvcTgb33rZMlfHLLkr6/i+OYMczR7BzT1/GGGcXM/mCMVXiKUbpKxW72sxmk+g1\ns6atDrVlPF0sm2HvFF45dEbyXnXarXl59lPROH6z+zRoKyW6DyNX8hPKC8N6yEqHmmtFKHwr1say\n9ea0Xm9331jJyBaqoWOZR3U4T0x5i0um0DsgnB+10RQqbRb4LhlqOco51Cg1q7unfxw//Mq1AISn\nDFFmU8Gu62LT1eeVnJjW3lqLN3tHFBtl2mJGLEv5hI0ncfjkCI71jcJTYxeMOpApZoRSwbAGGVA2\n1FwtZhOwvr1RMHwrJf+YFmZIobvPq1r2cK5gLGasW9OgOlQtlb8/3jcmGnKNxTk8em8HfIEo/uml\nE7L7KedQo1wINRSOiU4Z2raxBRyXxMHj50syL6wGX4CFLxCVHHP40YWQoi4Ft5NGCibEgrPPazSW\nxLnRkOyUNwJhLjG0QeanMH3mxsvxwn+dQpeIR6KG9WsacN+nls36u1wbSyyewBs9czNEXiuVdivu\nXN+sOk8rZWwmpljUiAxGcDlt8Ljs8LjssNFmWZUzsVBjOUyMUiqGIjRliDKbcd9tywGTacakIzma\n6isxNDq7OGqu2XP0HO67bbnoAuS796zGX/7LW+BkVh+M1YIRn7Q2QDiayAiplPL1QZifGDaHDEzn\nMZ98/j1094+Btub/dZOplKCEpJQRGg9EcajMjDEw7b2oRSon6Hba0C4iAsKHDhkrhRtXLpLdT26o\nUSpvXWpI1RYoDaFu29gMR4X8mrq2yoZNnU3Ycf9abGhvKLne7uP904Iz/AIk+/uHoxySCkIB0VhC\ntm7EH4wiwiZm7YNAKAUMbZBzi4f0qDQ9ePyCYDGRXBFZuUYWXz96VvVn5IzN9lvbsKmzCbVVNphN\n0wYjO3R4zy2t2NTZBLeT9xTNsNGU6PsBaeWrUmTbxhbZ8yDFrn2DCEWkhzLUOGg8/kAntm9qA2U2\ng6LMMJXYXe8PxST1yisYC2oUFF1NTsWwfIlL8j2kgItQyhg2ZK11Qg5tTfsPsbi4CRUqJip0Edlc\nceT9UWzb2Kbam5DKCfKpBKHwZHa4Ofc9AERD0XIpg1Is/pI6D3KwcQ7H++RTMIGpGCJsAk47jRf3\n9gu2WZUClEBvU3aRpJJWL5fThntubUOFzYI3ey8IVu6TAi5CKWNYgyzXh1xTSWNiKpZRL3I7aSy/\nzI3tt7bi92+ckXxw+QJReP1hNNU7Z/x9681LcfrsREZRyGSC6PjGciEa4+CdiKDJ41D1OSXGJjs/\nKjVoITuHKlbAVc59pkJ5YjkmQywmFBgp2krBYbeCjXM4fKJ00yaj/ghqq2cq3+UWScrR3lYHymzC\nprVN2Hz9Erx04AxOfezHRIhVXcCVvTAExBeCBIKeGNYgSxXN1FbZ8MgX2zHqj6DeVQEumZpxs8nZ\n0BSAn77UO0uc4qUDZ2ZUg5a7Mc6QxxdRamykKtSVyBrOt4lRSgVnojEOrxz6CJ9c3aCq17vYLKqr\nnPF/NREuG03hxpULkUqlsOOZIzMWdE9+9TqEwjHFxjR3YcjQac3waCxpCEEaQmlj2KtKKo9pt1nw\nt7/twt+/eBx/+9su7Dk2BC6ZxKg/jGA4Jtofmk1uflKPIfKliI2m4CmwZykXblYylUePIqlygrFS\nWNUiXByXS3ffGGKJ0jXGAPCfb/9phqCOGqW9SpsFqRSw99jwrPqBVw6dUVXAlVuHEI1xmWr/Uq9J\nIJQ/hvWQAeE8pt1mmeHF8jfZm70XwMY41DjUSRPy+UkjaVZnc91V9aIPM73ai/QKN0vlrY3IprVN\nitqe/MEoaMqsqJVsrth3bDidKw7G4L6kb+1y0ormH/sCrGg+XU39gNJFdanWJBDKH0Mb5Nw8ZgVj\nwQ+ff0/wvXw4T61OMG8wjKJZncunrlky629S+V4toTy9ws35FEmVG1wyid3vKauA5/Wcr7tqAQ4e\nv1DgI9MGL6EKTOuWN9VXKjLI1Q5aNJ+uZkGndFFd6jUJhPLFsCFrYFpTGgBqq214cW+/7l4sbzDK\nVbO6khE3WLVVNrirbLP+rnd7kd7hZqFeVqOxa98A3lBoXO02KxgrJbi4KmUujEmLfPDYbRZUVVoF\nX1OzoFOqf2/EmgRCaWBID1nIg7PbrIrk97KpcdAITMUEQ9082QYjN2Ra42DgE5DxKyVoKwVXlU1w\nvJ2QMSxUe9F8CzfnAxvn0HV6VP6Nl5iKxMHGObirbCUdts5FTpmL57yE4V6+pEbx/pS2LhqxJoFQ\nGhjSIAtV7KoNJddW2WZI7Fko0yUjL24wKLMZd65vxidXNwCpdOX2wz9/S3JO7VzjD8Xwl9vW4LX3\nzuJ4/xhCkQRqHDTaW4WNYaHai+ZTuDlfJkOsolAuz0SIzZzTVOleirpiu1QdffjkCE6d9WNVcy02\ndS6WnaWduzCk+f74WHpBQxaJhEIia5AjkQgeeeQRjI+Pg2VZfP3rX8fy5cvx8MMPg+M4eDwePP30\n06BpGq+++ipeeOEFmM1m3H333bjrrruK8R1moFe1c3tbHZx2Gk47ndnuprVN+MyNlwvq4Ap55cuX\nuEraGPP83W+7EGETmUEFE6EYegbGQFHmWXnhQrcXaenJnW9UOxhUV1oxORVX9H7+d5kMsWATpX89\n5gtjNc9o8RoPsNjffR77u8/Lti4JLQwB0odMKA6yBnn//v1YsWIFHnzwQQwPD+PLX/4yOjo6sH37\ndnz605/GT37yE7z00kvYsmULfvazn+Gll16C1WrF1q1bceutt6KmRnnISA/yqXY2mS5pLWetgqUK\nmLIR8soPnyxdIYZspqKz5Rd9wZhgH7BUWI+E8ooDY6XQ0ebB/u7zit7f3lYHC2XC7vfOZYRwjIzU\nIlhpf3vuwpAsEgnFQLaoa/PmzXjwwQcBABcuXMCCBQvwzjvv4JZbbgEAbNiwAW+//TZ6enqwcuVK\nOJ1O2Gw2dHR0oKurq7BHL4DSwgwhvrL5Sjz14HUZ3V9AWQGTUXuQAeE+4Hw1mIXI7kElyLP91jY0\neqSNRPbvsmvfAPZ3DZecMRZQzAQAUAUuN+067SXXGqHkUJxD/sIXvoCRkRH827/9G770pS+BptOh\n3NraWni9XoyNjcHtdmfe73a74fVKGymXyw6LRX+P6qbVjXj10BlVnzGbgJuvvWxGyDUQimJ/l3CB\nR+/gOB74DI1wNIGEKVXyxVta8QejoGgrPDlKSt++Zy2isQT8ARauKgY2Wls5Ascl8dz/eR9HTl6A\ndyICT00Frl+xCF/+zNWgsp7KeuxLKx6PU/5NRWQyxKLvnB9f+79W4Yf/6wg4AYewttqGn/6PmzOh\n6m4FutdzgdgCIZkEbulcjBODYxj1R1Rts4KhEGGlja0vyOI/Dp7Bf797zYzrzCiU2jVrFAp9XhU/\n2V588UV8+OGH+O53v4tUlpRiSkRWUezv2fj9ytoa1LL5uiZ0nb6oavZrQ10lYpEYvJHpYpnH/l34\nYQektXe/9fR+TITSYex03sp4+blkCohFWXi9wt/NAiA4GUFQ4/Z37umbEf4e9Ufw6qEzCEdi2L6p\nTfeeZ7V4PE54vVq/nTKUCqzEEgk89atjiq5rfyCKj4f82N89jKOnRgXnT5cy7iobtq5fiq3rl8IX\niGLPsSH0DIzBfylSJUaTpxIti6txoEs+nL/v6DmYUkl88dbZ883LmWJcs/MRvc6rlFGXfaKdPHkS\nFy6k+x2vvPJKcByHyspKRKPpObkXL15EfX096uvrMTY2vQofHR1FfX19vseuiZcOnFE9iP272ztm\n/D8YjuGizILBH5oOYxvRGPP8x/5BALPDyvmGmZVIZpbbSEU15M5vfvQXR/Dr106Lzm/+0a+6FF/X\nLieDPUfPYc/RobIzxsDM2di11QwGhiZljTEADHmnQJlMmZSKHIdPjJDQNaFkkPWQjx49iuHhYTz2\n2GMYGxtDOBzGunXrsHv3bnzuc5/Da6+9hnXr1mH16tXYsWMHAoEAKIpCV1cXHn300WJ8hxmo7dHk\nCYVjcFZMiwsMjYZU59tsNAU7Y0kb6hLL1eXDsVMXYTab0DswNqOveyoSy0gdavFa5VqovBORshup\nKEe2N/zywcEZ0QF/KK1QNTA0iccf6JxxLoPhGIa9yvvor2ioQu/guK7HXixsNIVUKgUumUSCS+HJ\nXx7FBZ/yaNrx/nE89eB1uHN9M3yBKP7fg4M4JhKy1zrNjEAoBLIG+Qtf+AIee+wxbN++HdFoFI8/\n/jhWrFiB733ve9i1axcaGhqwZcsWWK1WPPTQQ/jKV74Ck8mEb3zjG3A6i5/HUNujyfPHdz/Glz59\nVeb/TfUO1RWpsTiHR+9bCwD4p/99XNNxFJvGukq4nDROfuQXfU8oys3QTM7t61Y7mYlHroUKqVTZ\njlTMJTf07nLSCIvkOc+NhrDz9T7cd9vyzN/ULhDjca5sZVyjMQ57jw2j79wkQmEW/pCy9i6e8UAU\nvkAU9a4K7O8exuBwQPoDRlo9E8oaWYNss9nwD//wD7P+/stf/nLW326//Xbcfvvt+hyZRqodDNwK\nRemzOfL+CLZvWgbGSoFLJvHq4Y9EH4CUGYK5ZZeTgaemIt2Wsqxe1SzXuSIwFcOXNrdJGmQT5EdS\nAuq9VrkWKo/LbpiRirltcXLXZ3f/GO7eyGXOpdoF4rnREBiruSz64MVQq6yXzZ6j50BRZtl7sBjT\nzAgEpRiuvJA3hmqJJ4AX/utUxpPZe0x4io6jwoIFbuEbmNcMBoAt664AYy390xuMxPHPL70v+R6l\n/gPvtapBqoVKTuMaQFm0Smlpi5sMxWacS6edRqOKsKo/GFP+wxmQ3sFxRamrG1cuzDvtQVr2CHph\nSOnMbRtbkEyl8NaJC6qKrY58cBEVNgt6B8RbRGJxTlT0ntcMZqwUQuF42XgngbC4t8ZYzai0WRRF\nHLR4rXKSmUIa12taa5EUGEZfqoPjtYjVuKtmn8vH7u9QXGVd7aAxWYbFXHrhC0rXcVRXWnHNlQvy\n6p2f6w4AgvEwpEEGALPJBDtjQTSm7qF0vG9McgRjLCF+l/uCLHyBKBbVVmY0cMsdLplCe5tHNGKQ\njVqlrtx2H6F8sJDBfvngIPbmqKJpyWEXCy2jOYXOJW2x4MkvX4dgOIaPLgTgqLDg0IkRHBRQ7Frd\nWovefp/qcaJGgbGaEYsnBUP8LgeDH3z5mowsrlaE1PlK+ToklD6GXMbxN4qWoqp8H2B7jp4DALx8\nYDCv7ZQKCS6FeCKZE1ZmsLjeAbeT0aTUxSWT+PXuU/j+L97GI784gh3PHMHOPX2i7T7AtJQhl0zi\nzV7hsYNCqmKlAGOlsLq1TvH7KTPwmZuuEH3daaexqrkOSxtqYBKJS5sArGlTvs9Sw1Mj37IkRTQm\nbIwBYO1yT97GWEnLHgllE9RiOA85XxlLpQVMYvQO+hAMx3DqrHiRVLlxvH8Mf/d/3zgrrCwnaCH0\nOpdM4snnj84o2FHjWex8vX/G4IBsSrnyWkQhUhAuCeza24+v3nGV5PvYOIdDPcKLk0M9F/DTb38S\nA0OTeRVHzQVuJ4Pv39uBR//9HdHfmqem0gpnJYNwNA5/kEWNg0GYTQh+zmwCrr1yAbasW6pYjEUM\nqTSELxDFb3afxqmzfrMhI/AAACAASURBVBLKJqjCcAY5n+ESQP51MP5gFEOjobyOodQIhuMZQ5dt\n7MTCzFK5tZ17+kUNRNdpLz65ugGemgoAsyfssHEOpz72iR6ny8mUZOU1G+dwvF+ddOWxvlHcF18m\naSyGx0KiSnJcEhjxTeHxBzrx692n0NU3jlBEXfvQXFFZYYXTTuOGqxfIDtC47/blaG/1ZAxsLJHE\nE8++K/jeZCpdJ3J8wAvAdGmkYvra3LLuCoTCccUGWioNwdDUjMEyJJRNUIrhDLKWfF02+U7DcTlt\naKp35HUMpUZVpTVj6JR4FmK5NY5L4ni/uFiFL8ji8WffhY02I/eBuW1jCyZDbLp6WITlS1wlKRSi\nZZHIxpKyghWhKemUzGSQxa73L+L9j/yYKhNjDKTbnZ58/ijCUflj7hnwor3Vk1kcsnFO9t7LLvTk\nr803ey/Mut6kvFmplj0xylXMhlA8DGeQGSsFu82q2RjmOw2Hn6Os9mYtZZYtccFCmbBzT59sRalk\nbq1/TFHlr9ADEwDuXN8s+rC10RTuubU0vQ/Ni0QZwYorGqolX+85M443jguHtEsdpWH29z/yY2g0\nCI/LnpHa1HLv8SFuNd6sUAfAsiU1eFtk7Gopp1QIpYHhDDIb5zAV0d7uYaPN6Fxej7dOjKgyzmYT\nsH5NQ+Ym3baxBbEEV7YPxGzWr2lQXFEq5Q1OhmKocTCaCud470LsYfuJVYtgZ0rzcmasFFa11M1Q\nO5NDiWCFXCX/+2fEw/tGYTzA4vHn3oPLyWDtsvQCMdtQ+oJRTUJcSrxZoQ4AADh91m8IMRtC8TFc\nhYFcWFOOeCKJ98+Mq/aUUwA2dDRhfDIKNs6BMpux+brLNB9HKZEe3yddUcojNY/aXWXDyha34Gty\n8N5FIWYxF4NNa5tUvf/6FQtkQ5temeEnRkmZKMEfTC8Qf7enL2MoH3+gE9/eugouh1V+A7O2p1zk\nhg+XZ3voQqhtCyTMP0rTpciDfHPIXBKqtXMBwGox4x93dWMiFM8qFFmK6korJqfKJ38nhNVCKdaU\nlpPD3NDeqClqwHsXUkIi+VbOFhJ3lQ21Kq5LyiRflx1PSAvP1Djospz0lA8Hus/jU9cswauH/4RT\nH/vgD8bA0OqvhXy8WaFQdntbXckvGglzj+EMstYcUr7E4knELilz8eHcSDSB1S21eKNHOKdUDlBm\noG1xjSpNaakHUoJLqTJMPLneRXaFdzkoJqm9Lo/3j2PrzZzgwoJLJvHi3n7RfmwAoMwmrG6txcHu\n8k+ZqCGZAh75xZEZf5NrnRIiH29WTn2OQBDDcAYZSOtIH+o5P+fSlYdPjsDtpGFnLAiziTk9Fq0s\nqq2E3WaR9HpzHzZiubXxySiqHYxiw2RC2rOU8y7KRTFp28YWcFwSB4+fl02JSBUASWmt83DJFOLx\nFDZ0NKJ3YBzjgWg+h15U8u100AJjNSOeSOrqzYq1BRIIYhjSIJeSjjSvFiY2IarUGfJOYde+AWy9\neSlOfezH+bEpJFPph2ajx4GtNy8V/Wx6uLxtlve6prUO69sbBCUfeUwm4Dvb1mBpY7WkdyGnmFRK\nbSaU2ZweqWgyyRZ4iYVM1QjfvHVyBC6HFSuba9EzOI5JDamYuaDYxhhIF9E9dn9nZlobgTAXlEY8\nTyd4qTrKbIJZjTRSEbBayvcm7+7z4q9fOIYh71TmYZlMpVtTXjpwRvKzvPc6HmCRQtp73XtsGFbK\njHWrFoh+zu20YWljuq1HSn5Qqqpby/SpYrB9U+ulwjTxHKVYyFRtT7M/FMcbPSNlY4wLidQzYXIq\nDqRSxBgT5hRDeMi5OcRqBz0nq2wpYnEON65YiNNnJ+ALRGGag7CcVtL5XmEjIOWFynmvP/zKNfjT\nyJRgz+ma1lq8fHBQNi8sVcTncjKIXdIULqUHLR/S57ikoBLV4nqHaMi02sGgeh4Wa+lBQ10lhrzi\nk7J++lJvydUeEOYXhrjqcr2wUnxYuZw23HfbMjz14HX48deuxyfXNMz1IemCT8ILlfNeQ+E4Hn+g\nExs6GuFyMDBltTGlgFme9Z6jQ9i1b2DGdqTaTKaicTzx3HuKhlcUGzbOoXdQWLUsHE0gwQmv1qS+\nr9FwOdLDS/K1jTbajE2dTdjxZ2uxuF5c+UzsGiMQikXZG+R8h0noiVRIbFWzO9OnWO+yw1xqMXWN\n1FSK60dL9SRntzHd96ll+JuvXY8f/7fr8dSD1+HO9c3oEdF+frP3wqwCudzeZNulNpdoLClpzOcS\nqcXKeCCK4TFhpSoumYSCjqiyQczY0hYzHr2vA9+9px35rqOuu2oBtm9qA22xpBeA7Q2orhTvTS7V\nqWEE41P2Blkup1ZlVy8KoBWpEHQkzmU8NDbOiRqcUqTKLp7ZWCPRHqJGJCFbXEHqN43GOPzu9b4Z\nf8sWgvjvW1ehgha+rEvpQSu1WAGAH//6mKBX/7u9/dinYDZ1ObBkoQNWi/BvFUskseN/HcE/7jqe\n935OnvFnfne+sO6hL7SLTuAq1doDgvEpe4Ms9WCrrbLh61tWFO1YpJzeIycvYueefoz6w/BORMpm\nGhRlBgJh4ZatxfUO3Lm+WbLoSouyVrWDQY2EutKps/4Z++OSSezc04cnn38PP/2PXlFhF18gKqtu\nVSwYK4Wrr3CJvs4lMcurZ+Mc3jqhra+4FFOiI+NhxGLi7i8bTyEmErpXgy8YxZnhyRnXjKemQjZ6\nQyAUm7Iv6pJThqp3VxTtWOSKtPZ3DWN/1zDcThq01VwyrVlSCLVqVVfSaLusBpUMhSeefUey6EqL\nSAJjpWCjrQCEDas/yM7o0c3tQxYjhdIq3OlcvkBWNCa7aM7rD88YvKGE2ku/SyKZxIEu6VGGxSYW\nT6qaE60VE4CnXzyeORfbNrbIPjdKqQiQMH8oe4MMSCtD/fIPp4p2HG4ng9amarzz4ajk+3x5aG3P\nNYzFDLPZhPc+mPkd5cQ41IgksHEObFxcSMVqMc8YB6mmhqCUREMuW+CUfc8MgRCVyeMqO43HH7gG\nTjuNMBvH4d4RWbnNYlOMRgN+oZz72xOJS0KpYQiDLOaFsXEOH/5JfP6u3nQs84Arl14mjbCJJNig\neLhdDzGOyRAruWhh40n87/0D2L6pVdOsYb2OM19oKwUTpI0SbaVQ7WDAJZPY36VODjYYiSHCJuC0\n05gMxUrOGM8Vh3rOY8u6pbAzFl0kLktZQ51QXhjCIPPkemGTIVbToAg1mExpEYv2tjpsWXcFnnj2\n3YLur9TRY+ZrBWORlU/c3zUMymySnJFc40gbIqHNlMJsWu9ERLGH+Lu9/YI9y1K4s3Khe46eU3l0\nxUducaIXbDyJH71wFE9+9VpQZrNmicty0FAnlBeGvmqqHQwcFYVbc7idNB67fy2+fdcq3Lm+GaFw\nvGyKtQqFHgUxETahSDSluy9dqS5ayd1aV9qFOwoG9bIxDue9IRzuUV/Mtbi+MiOMItbzXEoUM7Z0\nwRfGzj39s/7Oq/0pqcYXUqErtfY6QnlhKA85F8ZKoXVxTebBrTeVFTR+/vuTmdXxqpY6uJx0WeeI\n80WPgphqBwOXwyob3ciekQwI5wIpSrjgqxQKd5QsCBiawk9f6gGrIdx8fGAcf/kvb2KByz6vZiMr\n5dipUWz5xBVw2mnV3m45aagTygdDG2QAoCn9gwA2mkJdjW2G5ON4gMX+rmEsrnfMS4Ncq2AqkxS5\neTiHnZE1yLw0ZoJLieYCS7lwZ3JK/jqJxjhE87ickqm0N2gyKXLI5xWBcBxPPPcuOpfXI5lKzejv\nliv+U6KhTiY9EdRiaIMcDMdw+tyErtuscdB47L61+Nvfdgm+Ho7GsaG9AT2D44YPX5tNwCdWL8Jt\n1yyBu8qmySMQ8kxWNdciHJXP/fPSmNneTO5DsJRn08pJeVotgESxuSqIMRZmIhTDnqNDsEmIyQh5\nu9Ia6iWQDiGUJYbMIfNCEU88967uutaTUzGM+sWFPXxBFrdduwR/sXWVrvstRda3N+KB26/EotpK\nzUZOKA+3v/u8ZIiVtqYvWzXSmNlKYKXCG8fFi7TMJv2MMUEesf5uMdUuNSp0BIJSDGmQX9zbjz1H\nhwoyZKKmkkFTvUO0WMgEYPe7Z+GutomO16suopxnIaitYrCpswnbN7WqKoLJRSoPJ6Z6xljMsJeB\nNKbceZErtEqm0kWDhLlFytvVokJHIEhhuJA1G+dw+IS0+lE+rGmrg9NOi6r8JFPA/u7zoCiz6Hua\nG6vRVUZa1tmYAHx76yosqqvMu+VDKg8nVmXNJpKiBU6lkLtTWhw0GWJlC62aG6vhO6XP4BS5NrL5\nDiOinCfl7ZZyOoRQnhjOQ/ZORBCNFcZLWuiuwJ3rlwJIr443dDSKenLdfWPYfP1luHHFQridzIwV\n9L23tRVFMrAQuJwMYDJh5+t9ebd8SE6DctCieT2xc14KuTulrTDVDkZ28Mn1Vy/Aps4muBz5ecpm\ns6nsjDFjLe6j6YYVCzV7u6WYDiGUJ4bzkAtZvTLii+CJZ9/NeDy3XbMY+7uEJ++MB6L46+ePYiKU\n9pJuuHoh7rm1DXYmfcrtNgpT0dIIr6ohzCbwxLPviqo4qmn5kNITvvJyN94+KRzpEDMuc527U9MK\nw3/3gxJ55CsWVePDjycQZvMTt0mVmTW+7qp69J31F03rfXG9A1+8tQ2U2Uy8XcKcYjgP2eOyi3pW\nepDt8VQ7GNE8MQD4Q9Ne0uGTI3jl0BkAQJiNI8yWlzGeLqTikIK4UVQ7uk4sD/eFW5rB0MoeiIXM\n3UVjCcU5ciWtMNm55Xs2tcBCCa9smuor8YcjH2PP0SGw8fwMqquKKauITN/ZSdUKe2YToNZ+0hYT\nNrQ34PEHOjPpBKXerlCNQPbf8qmtIMxfDOchM1YKN65cVPCZsYd6z+OmlQuxqrlWsaQh7yX95rW+\nsmpDMQGwMxbE4vJFcjUORlXYWCwPt3NPn6LUQ42DxuMPdMJp17cAis8F9w6Ow+uPKMqRS7XC1DgY\n7H7vHHoHxjK5ZbvNioTAeEFHhQUP37MGP/zlUV2+y+qWOgwMTc7omy9lJqZY1TKaHcs8OKoy3+60\n07h7Y6sqmUuhGoE1rXVIAejpH8N4gL3kEJjAxjgip0lQheGuEC6ZHulmU+hdaYWNJfHDXx5F7+A4\nFtc7Mnni6krxvKA/GMWIbwpdp6SnQZUaKUBxxXplhVVTqC/bM1EzwSkwlR6goDd8LnjUH1HVWiXW\nClNZYcX+ruEZuWUxA2mhzBidiOrWx87GOPyPbask53WXEjWVjGoZzaiGiJMvyMLrD8t6stnerlCN\nwN5jw9h3bDizEIvGkplIEpHTJKjBcB7yrn0D2Ftg7zib8UC6WnZDRyNuu2Yx/uuds6J5QZfThj++\nc06XoevFpJI2gYNJ0SzecDQONs7lP+1JoTFS65ErIR9ZRCFlsFXNblVa0hOhGP715RNgaLPq+cdC\nvHVyBCaUT5X16tbadCRBheLdyY98qveTSgFP/eooYonUjFnJvCeb6w27nLTmVBOR0yQowVAGWe1s\nXD3pHRjHlk9cgZNnxB+8Vy914WQZiPznkjKZFccP/UE279YjqdBvLssvcwEARv1h3Qpx8pFFFArB\nT4ZYHFA5qUnvHvpTZ/2oLJNCwlQqheWXufGWSFGfnsQS6QtbSCqT94Z58pHELYWWPELpYyiDrHU2\nrh74g1EMjYYk99+5rB6Hjquf2jPXRFR4BXq0HklVX2djo82wWk3Y8cwRXcff6SGLmD3ST80Co1D4\ngyxqa2xlYZAP9VxAKpVOO6VSqaJVWwPAm73pWcmU2aTr4r4UWvIIpY+hcshSfa2FhraasajOLrr/\n2iobLlvgnLPjy4cUAItF2aWiV+vRlnVXyNYBeGrsONh9Qffxd3rLIkptr1jQVgpef3ROj0EpyVT6\nmovGOLDxZFFz39FYEr97vU/3xf1ct+QRygNDGWTGSmF1a92c7DsaS+IPR85KPsh5ha9yJC4z/s/l\nYHRtPQqF42AlqqwXuiswFREOIeohocm3Y9W7KnSRRZzd3sUoas/TzxaVSQJZgGLnvj886wdtpcCI\n/D42mpIULrHRFGw0ReQ0CaoxVMga0PMBpp7uPi9++JXrLv1beNzfto0teP+MDxd84Tk8UnVQZhM4\niadijYPGD758ja6tR3Jh3ijLYUJkfKEe+To+F/y1Oysw+KfxvPPT2bllrz8MmEzY3zUk2zJXWWFB\nKKK9itxGU1jb5sHhIuRjjYI/wOLpnd2iBXU3rFiInn4vWIE2wBoHjR9++VrQVooIjBBUYyiDzMY5\nHJ9DjejxAItQODb94J2IAKkUPC57JqeZ4FJgy2iMz8L/n713D2zivPO9v5rRzMiyZFuyZYxt7rYh\nAQwGQ7iWQCG3LbtsSUPChjab9HK26Tbd027SJjSBtmmbpKebpu22WbpJm+QloSXv4U325CwJIaGE\n+91AYmxDwsXYWLZlW7KkkTTS+4c8QpJnRjPS6Mp8/gJZmnk0mnl+z/O7fH/WIlzr90i+Z/pEK2iV\nJx09qQOtFz/moNuHMhMtmPykZrzOQOtVS8ThgkG8ued8TA3ruEoTXG4fHCJJXAxFwmyk0dWnfAE3\nfYIF//TFGSAJAq2XHFmNYecTlF4nuWB2ewOiCV58GZ7ZSGsJXBqKKSiXdTaTuoDw6rjUxEQm3l/9\n5RSeeukINm45iK272sAFgxh0sXCkkK2Zab65ZoZk3JuhdNh3pjvmO6rBG++3S06KDEViVl254N+a\nGsJhi1xTShKqYb3c48LUCRZRz06/k8VwkjvkB+6aBpIgMOhi0ViXnVBOXpLAzXbo42ui4QYteUsj\nFQpqh2wyUmBoMm3NJRIxY7I1ojIVnSEcXVKxdtmUrGfcKsEk0dkKQETWUahsRC6sn4tx77F+Dh+d\nls5GD//GIYy1GtHd70YIYfnEalsxuGAQT/zHAfQ7fbCaacyZWpl1pSSpkry2SwPiCl/FDBwKpEij\neX1XOz7rHoLD6YPFTGNcpQlOty8tbUnzEYYiEAyG4B/RBTDQJOY02GSWWwlbbS15SyMVCsog79j7\nadaMMQCcOd+PV3e2iopA8OIAckp6coU3PzyPDXc04NylAXTaXQiGwoZPpwM4gc2wEgEEsVaFi2eO\nBStDEGPPydiJMxgCrvQM40rPcOS1fqcPu45eQTAUwv2rpib+wmlCynsz4GKxcHqVYJx3dkMFWjp6\nk1rARbf47Hf60O/0YdGMShw425PT0q0WE42GCWU43dGXXs33ELBo5ljMmlyOUhONapsJAHBOhnvf\n5+ewaEYVzl0aEMwV0dBIhoIxyGqJgiydVYXDH/ckVfs4MOyTTNLhk434h/ZYa49o7DBXaL3kwLb3\nO2JkHoMhiCbtKkmoihde4HfZSppTyGX/6W586da6rO1eEtU237eqAUUGfUwy4Oz6coQADHtT6/YU\nzf4zuS/bOrvBho4rg2lvwMIGgthz8ir2nLwao9Q1q74ioRZ+mYnGhtvDCzwteUtDLWQZ5GeffRbH\njh1DIBDAN77xDcycOROPPvooOI6DzWbDc889B5qm8dZbb+FPf/oTCILAPffcgy996UvpHn8EteLH\nJEnAZimK2WUpRawZPB9f4jNuuWBItH1jrtDvZHFCQaJcdAwt3hUdjdQCqv3KYPIDFsHr42B3uFFb\naU743uhxq4WU2ElTQwWMjH6Uwtebe87jfYH3MxQBi5lBd4Jku3xlX0snAhl2dEWHXORUari8fry2\n8xzuW9UgufiUegbkkOrn00kujy1fSWiQDx48iPb2dmzbtg0OhwN///d/j4ULF2L9+vW488478ctf\n/hLbt2/HmjVr8Nvf/hbbt28HRVG4++67sWrVKpSVlWXie6imhnTwzLWU3d5y+vWyfg4tHdnLCJeL\n0hhmU0MF9KQOW3e1jXJFR8dxpRZQg8M+UHod/AGV/apiTZxHEHKhL55Vg9ULx6sSfxbSuY53c/IK\nX1ILlmKDHqwvfzL1lZLNr3b8nD3RbQIA8PlD2HemG8faerCksXpUjoJYOEZuLkOqn08nuTy2fIfc\ntGnTJqk3jB07FqtWrQJFUaBpGi+++CJ6enrw5JNPgiRJGAwGvP3226isrERfXx9Wr14NvV6P1tZW\nMAyDSZMmiR7b7VbPXasnCfQOenHh6lBKxxFqh6cUq5nBghlVcA77wfoCsJYYsHhmFdatqAMx8rT3\nD3nx9v6LKZ8r3ZCEDiVGSlA+00CTKDHSYP1czHfkXdH8ZzwshwtXh+BhA5g5OZwZrdcT2H+mCx6B\nxY/VzGDeTTZ81q1eu0ADTeKLy6ZAT4pPGG+83z5q3OcuOWLGnQqEToeZk8uxbHY1lswci7sWTkBT\nvS1yT0TTP+TFf4ncH14/B48KTSc0RuP1cYqkYgNcaNS9DQjfS0LvEyOVzxcXM6rOrWqOLZ9R67oW\nF4t73hLukEmShNEYdsls374dn/vc5/DRRx+BpsMiEOXl5bDb7ejt7YXVao18zmq1wm7PbKMHfqfx\nUUtXVpO7ZtWVY8NtU8EuF3fplJrCSk1qdPNJJ8PeACxmBsDo3eySxrGj+hhLd0qyRxK+GIqEsYgS\nrOc0FunxD6umQk+SkVW4TiQMEI2BJlBRJhxuWDyzStKtlkqHJ6VE61wLjWPQxaKI0Yt6fExFFEhd\nCAPDhbtLzhYWMwPooDj8FX2PpHovZeJeTNbdnMnn5EZEdlLXrl27sH37drz00ku47bbbIq+HRNI1\nxV6PxmIxQi8h/pAMj9w3Fw+u9uLR3+5DZ48rK4KBa26tg80WjlXWirzH6wvkdKZrNG42gLsWTcTR\nT66hd8CDirIiLJgxFg+ung6SJGK+Y1fvsGjYoG+IBUlTsFUUw+sLoG9QWFu5b5BFmaUYj9w3F4Mu\nFsdae/Bvrx9POM6ff2spJlaV4KW3z+LgmS7YBzywxY1VjK7eYfQ7xTs88eNOB15fAL0DHry99wKO\nfNwN+4AXtrJwLF7oWjrd4kledyycgLaLjpQ9RTcqS2bXoKXDrtggR98jqd5LatyL/PwTD8cFk3o+\n1BxbPiN2XdVClkHeu3cvfv/73+MPf/gDzGYzjEYjvF4vDAYDrl27hsrKSlRWVqK393pMtKenB7Nn\nz5Y8rsORHvnIrbvacEWk+XsmGBz0wM4IX1p+ZerzcxntYpMKDieLz82swuqFE2JW1f39o3eingQu\nncudDnA+P+wDHlHXoIcN4ExbD/ad7sKJNrusvIDyEgMYHdDfP4w1iyfizvnjEo41Gs7PwWoWz4Lm\nfH7Y7c6E41BCdCwu/rz2AS/sA17U2orhYTn0DUk3htAhXIN95/xxOHJWvI5Wh3xWtU4vDEVgYNCD\ni13Kf+foeyTVeynVz9tsZtG/x2sk9Dg8eGvvBbg9Pln6Adl4TnIFqeuq9DhiJFwSOZ1OPPvss3jx\nxRcjCVqLFi3Czp07AQDvvvsuli5dilmzZuH06dMYGhrC8PAwjh8/jubm5pQHrxQ3G8BHLcp6z6oJ\nQ+lgE3BHcsEgtu5qw8YtB/GDFw/i+e0tyJf8B+tI5jTvapVySdkHpTN/f/zKMWzcchDvHJCOn//3\noUsRVSs5NE6xxoxLzlijUbvDkxyilbvEsA948D/vaUx4rBCATvswfv7qccnj0TK7dmWTmydkJhE0\nHtYfxF9bupJasESrw4X/n/y9lK57MZG7WY6qXTaekxuJhDvkd955Bw6HA9/5zncir/385z/Hxo0b\nsW3bNlRXV2PNmjWgKArf/e538dBDD0Gn0+Hhhx+G2Zze7b0Qr7/XltW4LEWSgiU+r+08FyP8kE2J\nT6XwD5qcuJNLpOFDNH1DLPo+vgaSEBYXAYCT7cryD1Y2j1P0fiGEsqAXz6rG6oXjUz52PHLr5ll/\nEJ9cdMg+ble/WzI3gU3QtSsX0OfJSlUHwFpiwKz6coRCoRh1uKYGG1bMrcGp9l70O1lYzdczkeUg\nJyNfKVKVDUr0A9IxNo0wCQ3yunXrsG7dulGvv/zyy6Neu+OOO3DHHXeoM7IkYP0cWi/Jn7zSgZsN\nwOn2wWykIy7J4+d6RMXocxmrmcGcqTbcfetkwTKmuxZMQFfvMGorTZFOT5OqS2UfX6qLlBJ3PkMT\n4LggWD+nWkcmfuFRW12WFheckrp5WqLVnxD5kpsgxmfXcj/+vWD6GKxZMgmlJgZ/+bAD70cJifQ7\nfXj/WCdqbcUIhUIIheTl1EQjdC+muvtMJE4jt+4+HWPTCFMwSl1A9ptLALx8ows3TbSOUqLKN75z\nzyzU2kyi2tz8a4QOqLGZ8MSX58BspFFbWSxLWEWtOmPWF8STLx2JUVtKpR5SKgtaLUpNDGiKkLXw\naLmgbJHpCwSxaEYVWi860O9k8y5uPOTO7exxA03g/tsaYGQosH4O+0V016/YR0u4Asq03tW8FxOJ\n0yg1qpl4Tm408sM3JJNSEwOLWb2evMlSW2lSTcozm5iKKFnfIxgCLve48PQr4UzojV+ei3GVJhAJ\nBBasJQysIr+XgVa+4uYXCtt2dyj+bDYIiPnroyAJ4GirMrlLq9mADbdPxaz6cFwzn4xxPlBeWgQj\nQwEA7A63ohCZ3Fhtuli3og4rm2tRXmIAoQsnQ65srtXczTlCQe2QGYrE1AkWHDhzLWtjIAhEmpNn\ne7eeKm9+eB6rF0+U/T067a6Iu37zg/PhdPtw8ZoTb7zfjqu9ozPq+eQQoRX7oplVIHS6SJxKT+rg\nk7mjzod6SLvDLRo/j0ZPirv1xWisK4d9wINTCuPwGvLo6h3Gn/67FbfNGwefnB8xiv4h+bHadKC5\nm3ObgjLIgPJ4m9qEgtfF5vOpzaIQrZccuGdFnezvEe2uBwCzkUbL+T5BYzyu0oS7b52Mv3xwHkyU\n69ZAk1g0swr3fb4eJEFEJg5fIIgn//OwrHErSVDJGgn0GUuKKdTVlOJ4mzJ51YpSA06123NeIz2f\nCYYQ05RCKjkx7l+5yQAAIABJREFUHoYmc6JfsuZuzk0KymXN+jmcOd+f1TFYS5jIAzd1vCWrY0mV\nficLDxsQLXOIh9CF3fU8Uu5utzeA13eFk2Gi46heHweEQpEYMD9x2MqKYC2RN5HlQ5N4W1kRSAmf\nvp4IewcSuf3j6R305mUCYb7SN8TKNsYaGokoKIM86GKzviOdVV+BN/ecx8YtB3HgTDcMNAkDnZ+X\nuaw4vLhYs3QyGBn1q9UVxZFsa0A6ya5/yIu9IvXie052ReJsrJ9DV98w/ry7HW6ZbQjzpR6S0otb\n236nDyEklgvVyA0MNImy4rDDscQo7nj0jZQOamgIUTAuay4YxM4jl0VbH2YCggCCwRB2n7juLsym\npnaqzKovB0OR6HG44ZNRv/r1v50e838pt31JMYXBYWEDywVDuHLNiUOtPZJKXQaagK3MiGGPHwMu\nFhazAY1TrFjeVJNyCVS6GXSxGa+Xp/Xy4/A3GvNvssFspHGyvRd9Q2xkHpGboe71cWBHHBMuT0DU\njW0xMznvvdHIHvm5dRNg2+4OfHC8M6s7imAQsnsH2yyGNI8mdXw+Dqyfk5W9bjHRsJUVxbwmpeoz\nqapE8nj/52BipS4jQ+HxDXPx9NcX4MdfvQWNdeVoOd+HjVsOYeOWg9i6qw1cMDf9iaUmBuUyXfBq\noAPwgw3NsJXm/n2XDTquDEKn02HmlHD+Az+PKJlO+PcGQ+IxZaOByumFohisn0OPw53VDPEbgYLY\nIedSidGgS178zu7wxiQz5SL7z15D6yUH5kytFO3MxOPngjG9nu0ON6DTYc3SyQBGq/rctWACTv5m\nn+jxLl5LLMbhcLKR5K0PTnTGJDJFN5xXUveZKaRqQtOBtcSAKqsRP9gwF/9T4rrfqPB1wsmU2ylh\n2OPPee9NNFrv48xSEAY5X0uMctkY8/ATFZMgDu72BjDg8uK/DlzE/tNdEXesgSaxeGYVNj80Dy63\nP6bMotZWHCOewFNlLcK1fmlNbOB6xmq+toRbt6IOwVAI+093pz20wcfVGYpElbUI3TKu741Iun+H\nAReb+xUAUcSLG+X6QjffKYglDh+r1EgfbIJ4ZzAEvLazDbuPdcbERr0+Du8f68SOvZ+Oavaw8Sth\nARE+tUmHcDnU4xvmKPo95Wj0Zho5Lj6SIEDodKobAYIIN5HQiQg/fG31dIlPa6STfKgA4FGjGYXQ\nMTXXtzgFsUPOtPtPQ5gLVwdE//ZRy1WsWToZxqi2lLReHxEQudLjitHElvN7+qKaXcjV6E22MbsY\n8cdT4uJLV6glGAR8wSDKTDQap1hHndtizg+DUIjkSwUAoF4zCkBzfculIAwyAKxZOgl7T13NCzdw\nPBYzjVAIGJAZf85VBobFNYi9viBef68ND33h5lF/MxvpiJgIz/WOMuJZ1paotpBiBnzq+HArP7Un\nBLHjhUKhmEYDQi6+SE/sQDCtoZYBlw8fnLgKkiRi3It/zhNp0ULAbKTgcvthLcm/jkhqNaMANNe3\nXArGILvcfvhy2BgzegKz6spxuHX0jshRIEIOFKmDnxPPSz16rgcr59WiylqccJcQLfH36s5z2B/V\nupInercR3RKuf8gLZiQ558CZbpy75IDRQOFyjyvy2VQnBLEJRiwp6ERbL9YsnYwdey9EjLjFTMtu\nMJEK0XF01s/h7Gd9aT2fxnWcbj8sJgaNdeWq7AbV9vBIoVYzinzN8cgG5KZNmzZl6+Rut3qGSK8n\ncOBsNzxsbsYmbm2qxlfunAYPG8Cgywcvm9sdbZIhrLss/ncuGMKek1dx8Gw3ege9uHmiBYROB9bP\noX/IC72egJ6MnbD0ZHghE7luvgAsJgYLZ4TlNYkRCUpCp8PMyeVYNrsaDieLT7ucCIwsDjwshyGR\nPs2DLh+Wza4edV6e4mJm1H3K+jlsfa9N8F4LiCxIWF8AA04We05ejXzO4+MU61QnA+sLYMnMsSgu\notA/5MU7By+l/Zwa1/H6OHzW5YSHDWDm5PKE7xd6HrhgEG+8346t77Xhv/ZfxIG4ZygaoXs2WW6e\naIk8e6wvAGuJAYtnVmHdirpR5xWjf8iL/9p/UfBv0fdmrqPWdS0uFvcsFMwOOVfiyDodsLSxCmc/\ndYxqTB6963v5nU9w+BNlXXxynQAXwrybK3HkY+nvxe8mQ6EQdDpdQjcySRBYt6IOHBfEifZeOFws\nWjp6QRI6wV2Hkp7YyeheJ5PVX2ZistarO9q9WGpiYDFRcLjkqZ5pqEei3aBUWCVbLl81mlGo6fou\ndArGIHPBINze7Lt+QyHglpuqsHZZ3ahEpWhaL2Znck4nFrMBD955Ey53u9DdP7qhRDz7osqjgFhD\n/Q+rpsa8d9vuDnxw4uqo9wKxE5JSY5nMhCA1wRhoUjBretoECw4IuN0zQbR7kaFIzJ02JusL1xuR\nRIs/MaPLcUG0nBcOM2TK5ZtKMwq1+zAXMgWT3rZtdwf2n8mNHefh1h786I9H8Is3TuJHfzwySjFq\n0MXC6S68HUpTQ7j/rpeVtzASk47cd7o7pixCSfmF0hK4ZBuziymQLZpZJdhvdv2q+oyU5s1psCXs\ndbtuRR2apyV2nWqoi9TiT+oePy6R2Jitsj4lsH4Oy5tqsLypWuvDnICC2CGzfg5HP8nO7kOIPSel\nd3KF0JoxGkIH1NjC7RQHnKxktrUcvD4O9gEPam3hzlFKyi8YisS08Rbsk9iN6oCUs16jk8iiFch4\nF/rqRROTKuVKla/cMTXSj1vMvUgSBB6482Ycb9uLHFUWLUgYmoCe1AkmZknd42Ka70B2Xb6JEsyE\nXPCNdRVYObcW1hKDtjMWoCAM8qArdSOQbqJdS7kS71aLYAi43OPCXz44j7tvrYPVTKfeAjB0PdlJ\naQzqvlUNONbWI7gDLy9h8MjdjbDFiZQoRSy2xgWD2LqrTTAOKGTEZ9WXIxgM4XibHUMSE69cXB4/\nxhrphO7FHXs/1Yxxhrna68Yjv/oIBoaEI+7eSHaRnmmXL59wtuvoZbSc75PM/RBywX9wvBMkodNK\nnUQoCJc1lwcNSaNdS7wLZ9nsasX9bnOZD09cBRcMYs7UypSPFW1kpVzEQhOSkdFjSWO1yPttqK00\nqzaJ8bE1/nj8JNQ3xCKE6x6Sbbs7Ikb8J1+7BT/9+gJsfmgeCJ0Op8/3qWKMAWDX0csJ35NL2u83\nGm42gH6Be4OhSDROkR9GsJqZjLp8+YXmxi0H8cSWQ/jgxFXBe5wnHSpfNwIFYZDPdw1lewgJsZgZ\nmIxU5KbeuOUQzlzoQ2Vch6R8hguG8Mp/n8O6FXWosaWm1euJKwtbt6IOK5trYTUzYZdzgglJ6fvV\nwM368VFLl+Df9p66CvfId+KN+I69nybsaKWUlvN9Mb2khWQK81X7vVDhDdTK5nGy3q8D8J17ZmH9\nyoaU6pqVyFhufa8t4b0abWhzUc42HygIl/W5LJWTKIH1c5EJmIe/ubPZw1ltjnzSAwNDwuNNPoRQ\nXiLeM/Z66WMIbm8ArJ+DkRGflPj3yyyZTImt77WL6lKz/iCe/tNR/Oir80ESRNp2qf1OFv1DXnxw\nohPHz/Wg3+mD1UxjztTKiEuRb6eZclhBQxV4A2UtMaBchtuaoclRrU6VoES1Lrwzbo/Ji0n0PSot\nxqzK2eYzeW+QWT+Hjz/tz/YwEjLsCeBY6zXBvxWKMQbCPWH/elJ4lyiXWfWj3dDx8ah+pw/7z3Tj\neJsdSxrHRiYTXhf7UOu1mHGku26T9XNovSh9H3b1u7H1vTZsuH1a2napVjODd49ewp4T178737Er\nGArh/pFyMprSA9AMci4gRwI2mmAoBF8KLRyV1DTzfeblEG1o9aQORgMlaJD5MJOmbz2avDfIgy42\nL0QOQkBejDMXiN/MSrmCvT4ubGyCQbRfGUKn3SW5wElX3eagi5UlgXqivRf3rODSlmnfWFeBfS3C\nE+i+li6EQsCpdru2OxaAoYmEXc3SgZAE7LFWOxwibl2fP4hNLx3B3GnKjZcSGUulXpzo77Ftd0eM\nVC3PuEpT5Dtq+tajyftlSKmJAZUH34LQARZT7srD0WTuZJedbO+LiWtJuYJ59pzswuUeaWMMpC9+\nJbf+ecDlC2ttUyRm1Veodn4DTeLWpmp8/GkffCLRAtYfxAfHOzVjLIKBzuz+hCR0+Pzcmpi8Bj7x\nb9OD81BaLD5fOFyjE6nkoCS2K9eLE19TLGXI3d4AAlxIS/oSIQ9MmQzy4FvU2EyYXS+cKZwLmIy5\ns1joH4rNSE/kCgYgWxNa7bpNPjEGgGgmeDy7joV3AUGVYhWPrW/CM/9jIc5dHMA1h1eVY96IDGa4\n2xoXDEvHCsVt397/maymI0qNVxGjR6lptHIgMPrZSLTIZCgdFk0fg80PzY9JMJNj9LWkL2EKwmXt\nz6ESZEIHVFqK0N3vibxGEkAoFMKpjt7Ie3Itbqz2rslAE6JKXInQ6YCdRy5j/cp62a5guTTWlafs\nrharxZxdX4EVc2twsq0X/U7xCaWlow/OJT6cak+965IOwNZd7XB5/HBInFMjNxEKocS7cqUQk+OU\n6tMt1uY1voQwUUyb9Yew/+w1GIuoGBez3IQuTd96NHlvkIuY3PoKwRDQM+CJeY0LAlfswzHvAQCT\nQQ9XCtnIuYiBJrGkcSyCoRB2H5OXDBJPMISIgMDaZVNUzQheObc26c9GT2rxE0nfEIv3j3Vi+Zwa\nfOdLjbAPevHrN08LHsfh9OJKjwsDKuwCQoBgrE6IVBZJGukh3qAqjdvGGy8uGMSWHaex71SnZJ/u\naMolVOvk9CWPX1TwNdXR2vM80UZf07ceTW5ZsySIr1fNBeQqINEUAZ03PKkWCkU0ibXLpkBP6hAa\nMaxi6HTALTdX4vDHPYIeA/5BLy4SN8j8ZNJ60RGz6BF7r7XEoOj7RCNn57LnRCc+ON4Jq5kWbTRh\nMRtQW2nKqHzq52ZVgdKTopOyRnaIN6hKs+/jjZfSPt0WE4MnH2gWbIADXI9pf65xLJ586Yjge/qG\nvLjQOYjJNaXQkzps290RaYbBewOtZgZzptpijL6U/OyNSt4b5FzbISvB4fIVlDEGwklLvKusucEm\naZBDIWDpzGocOivcFMTh9MI+4MGwR3x3PH1SWbg1YzCIp185LpllnczK2+sLoMfhRhGjl7Vz4c8t\ntaNvrCuHy+MfuXfTb5BrbcV44M6bwQWD8LAc9mep65TGaOLvyVITgzITI5phDYQXslYB4yW1uxZL\nihwcZuFhA6IGmcdmMUrWSD/3xkmUlzAwGqgYjw3/PMyqrxiVOa1Ga8dCI3+t2Qi5uEOWi8XMwOfn\n4PLk73eIp8xE451DF9HS0YcBlw86iHsACB0kd4oWswEIhSSN219PdYOm9Fi/sgGbH5wPp9uHi9ec\nONp6DWc/HUh65c27p1vO98Hu8KDURIvG3qQI5w+EJyZCBxgNepxqt8uu7UwFHcLX94kvzxkZS7iv\n9MGz3TmXw3CjYKBJ+Pyc6D3JUCRmN1SI3h+EDvjhA/NQZR2txZ5MbbvFLC7Cw8MFg3hzz3kMe6XL\nNvuGWFGDzedNeNjAKMObSmvHQiPvDXKpiYFBD+RjKNbt9RdcTM/PBWMEOaTmfd4oSMWS5CR3RMew\nzEYaMyaVY8ak8pQUgOJdf8kYYyCcP8ATDCFji685DRX4yh3TRu18PGxAM8ZZgM+tWLN0Mlxun+Q9\nuXbZFPz1ZCeEJPqDIWDnoYv4yp03jfqbVDIVSUDweEYDlfDZUJJkJkbfkBdPvXQYgy6fJgAiQd4b\nZIYiUWMz43yXU/Vjl5cw+Kc10/Hc6ydllSBIUWwgR+rvrh+n0IwxoNzgXOlxScaSuvrcCY/RN+RF\n/5AXY8uLY15PduWdjKxltjLnaUqHMZZiuL3hLOv4NpD8oqSI0WNw2AefP4ASI4WhAuzHnYvQegKz\n6ipw18IJkV2tMSrMJrRodLl9knkoBz/uQfuVwVFGTSorWk/qBEsDhz1+sBKqX2pKvPKLWk0ARJy8\nN8gAsG7lFPz01ZOqH7epwQZbmTGlrCuaIrBwxhjoCUJLqBGg0lIkHUsKybv4u45dwYbbpqoypkSu\nP4uJweBw2Pg11pVj5dxa7Dp6WTCrNN2EgiE8vmEuAIi2gSyUvtv5BgHAFwji2LkeHGntQXnUzhCA\nqGyknDiymFFbt6IOxiIa+05djSxup40vE+0PPjBSExy9cI1eJKSzEUm6VPPymYIwyB+eUCdJhdCF\n539rSbhPbYAL4od/OAw2oGwnu2x2NT4/txYIhWAbudE3bjmoyhgLjehVu9CO1mYxyirXaenoA7s8\neX3faKRcf+UlBjz5QPOoWNjaW6dg/5nulD0pSvFzQGevC5PHlsZcOzXcjBqpwd8J/C0ebUQBSMpG\nTp9kwUenE89r8UaNJAh8bc1M3Dl/XMSoAkDrJUfCml8hbenGugrFZYfjKk1wewNwOL0oLRZfWIjV\nUN/I5L1BZv0cPvksdYEFAGieVolls6oxtqIY//bnU7LrO+O5bd64GPdpj8OttbsTgNYDpgTZnQxF\n4pabx2BPgoYVaj7cUq6/poYKmI10TGyWCwax9b32jBtjnhf+cgrzb66KcVNr/Y5zlxNtdoREPD+8\ngb3lpjGyDDJ/3/O7Wd64xi9u5dT8CpVMfXC8EzW2YkCGQY72AAS4EOwDHvj8Afxux1lNAEQmeW+Q\n1Wwu0XFlAEc+6QFNEUlPriYjBVNRrAxluhoJ5Du+ALBj74WEcaTb5o1PaJBLixlVS+B4t2LL+T70\nDngkM7W37e5IqpRIN5KCnmroecgdiNldaf2Oc5v+IVb0N7/eilGeobKYGew8fClGMW7xrBqsXjg+\nJmEqUc2v1CKuM0F9PwAsmlGFDbdPjYRL3txzPrLTZmjhxK1cFQDJZjvIvDfIpSYGFhOlilHm3TKp\n7HRcbj9+9McjMQkXctuq5TvVFUaQBIErPS7ZRub4OTvWLpsCAKIPgZw+sQ4XO+q6A8k9XPxn1i6b\ngm+snYXzn/WJfj6V3ajM8LhsPmrpwpqlk7QFYI4TAkTLAfldY8flAVnHMhqomNyFviEWb+29ALfH\nF7PQTVTzm+wijtABy5pqsH5lfeSZi99p8+GmRCVf2SYX2kHmvUFmKBLTJlhx4Kxwr+FsIJRwsW5F\nHfyBIPaeulqQZSe1tmLUjyvFB8eVJTY5nCxe3XkO5y45RB8ChiJRX1uGvo+lf+Po675m6WS8/l4b\nWiWOG4/QAym024ims9eVM4bP6+Ow9b12fPULN98QC8B8RmwK4HeNRxMs8hiKwKKZY3GqXV4rxeuf\nE648SHYRFwoBt88bF7MAFlugGhk9Ht8wF7ayopzcGedCO8iCKAJjRGThsg3fiYWf6M9c6CtIYwyE\nDfLJJHaKlF6H/We60TfixuMfgvi2crffMl72MT9q6cL3fvsR9sk4bjT8Axn9mbf2XsC23R1g/Ryu\n9Dhxxe4C6+fgCwTw1EuH8dNXjin+zunk7Kd9cLp9WLN0EhbNqILVLB2j18guhC68W45uYcj6OZy5\nIJ0XU2ygsLypRrTxityOScl0K4uG0uti8kCkdtoDLha0Puwx5M+bK20Wc6UdZN7vkFk/h9Pn1Unq\nUhv+odh17EpO71aMjB7uFBXPDn4sLH+ZEJE2zPEr/CqrUVQbOh6p94jtHKQeyL2nrmLvqauRUIaB\nJqEndTmpsDY47Me//PqjcB6ELxje5c+owtpbp8Dl8cM5zOK5N05le5gaI4QAfO/e2ZhcUxq5J/sG\nEyeBDrhYIBRKumOSkDeI71Z2qr0P/UPeyPik8AVC+P7v9+MXDy8Crdcn7PRkMlKRcrxsuYWFkNMO\nMvm2NPLJ+x1yOhNYaFLEWsjEYjbI1kDOJm42AFNRdtZmPr/wIx+/wteTOpSXJt8YQuy4PFL3EesP\nxgm65LbcaTAUjtvxu/x9Z7rxzsGLqLWZILoC0sgKDEViwljzKD3rREldFrMBNotRdFebKGFKyBv0\n/rFOEDodfvK1W/CzbyzArXNqZH0HlyeAzX88Gvk+UmPasffTUedN5LnKBFLXPJPZ4HlvkOXcvMni\n40IpGaqmhgp42EBeZLy6s6Q9ajZSgq/HPwTbdnfIyvZMBE2Rgg9XOu+jXIB3u1VairI9FI0ovD4O\nO/Z+GvMaQ5GYMblc8nO8l2bdijqsbK5FeYkBhC7s+v7bpZMlE6akvEHHWu3w+TlUWoxYv7IeK+bW\ngNYnNhNdvW786b9bwQWDWLeiDivm1sR0mGIoAk63D8dahfNAMukWjkaOyz6T2eB577JmKBJGA5W2\nxJpkdkLR/UUDnLhbKZfIVmy7sc6KfS2jH9LZ9eWRhyATdbWFngnfPyQvpqiReYTCKLMml2PPSfEE\nyc7eYWzb3YH1KxtGZU/XVpfBbheXEpZ0z7pYPPXSYTRPq8S6FXW4f9VUfGHhRDz6u/0IcNKTxJ6T\nV0EQOmy4bSoInS4mdMT6gzgkEdbKtEhIIpd9ttpB5r1BZv2cZHu+TFNaTMX0FyUJpHXBkO98JqJB\nzkXVBHX3D6t2/bw+TvTBH12ryWC4QBqAMPR1z4DFTIsmA2lkHn6xFH1PGg2Jp+ZoQx6dPc23DBUr\n1UuUUT3g8sVkF79z8GJCY8yz50QnfAEOZy/0y3o/T6ZFQoQyqt8/1onFM6oElfgyRd4b5EEXm7HJ\npayYxsCw9LkGh/1wefwRg5xrC4ZMINZZRohOu3DziINnruHuZXXYsfcC9p5STyO6zESLPvhCtZr/\n9/BlvLX3gmrnzxa8MhRDkZg7tbJgPQH5CE0Ro+5JY5FwKCea+F1lfMtQsYQpud6gE229uH3eOBxr\nle+dCoaAfS3KRXIy6RaW8rjtO9ONTy72Y87UyqzUScuKIbe1tWHlypV47bXXAABdXV3YsGED1q9f\nj0ceeQQ+X9jgvPXWW1i7di2+9KUv4S9/+Uv6Rh1FpmJ/5SUGbH5oPr5zd2PC9+46dv1Gz+SCIVco\nLqJQZSkCkUL+kNfH4bWd57Dr6BVVJSm9vgDe3HMenEQ7HX63wVAkHlw9HbW2YtH3JksRTWY0Zs36\ngxGX9Zqlk0HpteSuXCF+7yl3ER+fD8Hv+nocnoQJU3zs2SKxK+0b8mLzH49INrlIhfhyr0yRKBG4\n3+nLWqJZQoPsdrvx4x//GAsXLoy89sILL2D9+vXYunUrJkyYgO3bt8PtduO3v/0t/vjHP+LVV1/F\nn/70JwwMyFObSQWprD41mVVfjrf3f4ZX3j2X8L0HznRHyohKTQwsN1gt6NCwH90OT8px6dZLDsWf\nKS8xSCbieX1BRQ+bnwvCk2JJmBAeH4dhtx8VGTLKhA4oYvRg/Rw6LjvgDxRoQXwe4htZLPEdujZu\nOYhntsrpXnf9N3S6fTjaKhyjFUqY4r1Bmx6chzKT+PyUrmqC8hIGmx+ch5987RasXTYFfYNeWUld\natQvy93EZSPRLKHLmqZpbNmyBVu2bIm8dujQIWzevBkAsHz5crz00kuYNGkSZs6cCbPZDACYM2cO\njh8/jhUrVqRp6NdZt6IOXDCIPSfUV8GymhnMmWpDKBSS7ebz+ji8/l4bHvrCzRElsWS0jm8ExPoI\n03pdpH+q3OM8vmEObGVGbH75cML3H2u1Y/WiiTFNIoRwDLFpi/+zgSDYITYjvZSDIeCVd1vR0tEL\nf+5WbN2w/N/Dl0ASOuxW0KLV6wui4/IAjnf04mRbr+jzIpUwZTbSaJ6W+RDGtPEWWEuLYjSvpWqS\n1ZS1lOuyz0Y3qoQGWa/XQ6+PfZvH4wFNhyey8vJy2O129Pb2wmq1Rt5jtVpht2em/pYkCHRcGVJt\nUjPQBEKh8MpVpwu3CBSTqBOj9ZIj0vh7/ap6HDvXk7VuQLnKuEoTptSY8eGJ0Y0jKsqKcLVXOL4s\nRI3NhMnVZehxuGWFCOKzScUeajIVv7tMMpXhfqy1NzMn0gAw0s4V4ZIfXyAIiSgJ9py4CjqJMML/\n+nNikZeSYlqy8cq6FXXguGBG+3nvO9ONY209MQmTUlKVasta8i7y4yMGXohsdKNKOalLrI2Y2OvR\nWCxG6PWpB/Idg56kWyXGU1tpwpWoY/EtyBSPycmCpCnYKsLxx8WzarD76GVVxqgmRQwJD5sZtwxB\nAAgBlhIDbpleha+vmYk/vHVG8L0Op/xdKaUn8L++vRRFRTTMpUWwWYrQ4/Ak/ByfTWosovG1NTMF\n36N0IaahwRMMAQtmjMXBM9Kdynh8aQojDLh8ePrVY1gwYyweXD0dJDl68fnFzzdk1CADEK1eaDnf\nh2+sLYKB1o+8L4AWETXG+PfKheOCMBbRkgvuxbOqUVtdFvOazWZWdB6lJGWQjUYjvF4vDAYDrl27\nhsrKSlRWVqK39/oKvKenB7Nnz5Y8jsMhfwckxW/ebFHlOAtmjEH7JeG4t1K3osVsAOfzR+oBv7h0\nEva3dOZMCQ3fuzQUCuF9BW6yVJg/rRJ3LZgA20jCVNe1IRxoEZ4ElCwS/IEgfvf/tmDDbVPBBYOK\nszX3nbqKO+ePE/zcxLElop15NDSksJhonLuorPwnXfQ4PIJdoHhXsJhgRzawD3hw+FRnRE60x+GG\nXWSB3TvgwfnP+hS7lbfuahN1WfM6EqsXjo+p57bZzJL13XKRMupJKXUtWrQIO3fuBAC8++67WLp0\nKWbNmoXTp09jaGgIw8PDOH78OJqbm5MbsQJYP4cLV9VJHls6Y6yo+0KpWzE+jd/I6LGksTqV4alK\ncMR/9rdLJstS4lHKuEoTykcSJww0AYYicOjjHvxqe0sky1lN2dMTbXawfg7bdnco9pZICfGXmhjU\nVprUGKLGDcZNE60YUODpUROxjd/R1h443ddDOrwrWK2e8qoQAp574yQ2bjmIrbvaYDJSqspaSpU9\nlZloPPlAM9avbMiKtnbCHfKZM2fwzDPPoLOzE3q9Hjt37sQvfvELfP/738e2bdtQXV2NNWvWgKIo\nfPe738WugYvuAAAgAElEQVRDDz0EnU6Hhx9+OJLglU4GXSwGhlPPUjHSJGorTaIF8+UlDBqnlKPl\nfF/CJJ/lc2oE0/ijhSf6RsTbs4XD5ceuo1fg8QbgC6i7a184fQyMhusa3kJxogAXxIqmGtHrraSW\nGQi75ewDnqQUvaRqkwHg+/c34bHfHYjJOFU6Po0bBwNNYtHMKqxZMhFHW6+lzRUthdgGYsDlw6aX\njmDuNBvWLJ2ckzr7/NCj48RiSVjJ1C9LbQSGhn3wsIGEyZ7pIqFBnjFjBl599dVRr7/88sujXrvj\njjtwxx13qDMymZSaGFjNNPpTrPVtmmaD2UhL/PC2EYPahiOtPZLlAMubagRXV9HCE3aHG0+/eizr\niV6tlxyqXD8eq5mGgdEndIN/eOIqPjxxFQZarDcxYCrSKyq78PmT0w2fWFUi+VD/779+OmocmjHW\nEOKWm8fggTungaFIvPbeuYwbY0IHjBtjRnefC6xY4xYXG1mM54LOPkMRkvPgibZebH5ofuTfqcpa\nJupIlelErmjyXqmLoUjMUUF56L7P1wMQkk+8/sNv290hL/EhQUIbQ5GorTSD0kvfiJmgf4jFwhlV\nqpVlzaqrQEuH/GxeqZi60oYXoSCS0g2///apon/z+gI5uYvQyE06rgwCCLtF952Sl8ylJsEQcLFb\nXpyz9ZIDFhUX48ny7btn4sX/72MMuYXd5v1DXrjcvlEqeskqe0mVPRkNeuhT7PKXCnnf7QkA1iyd\nBIZK7as8+YdD2LqrDQCwdtkUPHL3TGx6cD5+8rVbsH5lAwJcSNbEzFAEoNMlLCh3un0YzoEWfgwd\nLsta2Vwb050lGWptxVjZPE61VbfSuP3Oo5fROEW6S048RoMeHjYg+ns5htRt7ynmEdAoDPh8BLvD\nDVblUJDaOJwsGiZYsj0M+AMhOEWMMQCURoWUolX0pEgkILJuRZ2gAt/lHldWW0Hm/Q4ZAFxuP3wp\n7jR5ubRzlwbg9vpHFZ8rSUB66j8PJyxcv9TtzJnMXd6VvmbpJGx9rx0Hz3YnVRvrYQMwFVFZ627V\n0tGH7903S1H5htsbwBNbDkWyzuN/L6NBj1ITrUikRIx50ypxRERNKRNQegL+HDcS+Y7FbIDJSOPl\ndz7O9lASYjEbwGRZQpWhCZiK9JJzRlO9dJyY9XORXTMXDOH199rQeskhKiASVkRrx9Ve4XauQt23\nMkVBGORE3UuUcDmuBpl3a6xdNkXWOXgXdKLC9aM54gb1Rd3MLrcf61bURW5mpfQ7WXjYQNbaGPoC\nQfQOJHcPxP9e0UL9ahhjAFk1xgA0Y5wBZteXY8feCzh2LvdFWIoYEgfOZLncKQQ8/cpxMCKeo3GV\nJqxfJSz8Ea/exdAk/AEuJr9DaB7etrtDUlsiGwpdPAXhP0u3nvWJtvDDlcw5hPRQWT+HMxeEC90z\njcXMYOfhS9i45SB+8OJBPPXS4aRdtFYzg1ITgzVLJ6Xs/k6Wg2dTi4Xzv1e0UL+GhlzYQPp7d6vF\nFftwVjLAo2H9QYRwPZfEQJPQ6QCLicHyOTV48oFm0fIj/hntG2JHjsGJJlvyz7Wb9eOjFunYfjYT\nuwpihwyMTsbSEzr4ZPbwTAS/YuLP8VFLV0zzbTmfjV5tqVl/mypGAxXj4k1lN9jUYBsp5GfByrw+\natNyoT8lbWiH05t0+ZSG+kT/lkaDXnGiX6bZ19KdKKczIzQ1VOBUe2/GZFnVwsjo8fiGubCVFSV0\nUyt5Rvl5+K19nyWcuzPZCjKegtghA9fjoD/52i346dcXoLHOmvhDMuFXTPw5fvHwYiyeUQVrCQOd\nbqTUR2RHKLTaylTLyEQwFIEeFdTSSEKHz8+9XntdamJAZzF5SZ9CXMxiNgChUM4smG5kVsytwdJZ\n1SgxUtABYPQ6CKg+qkqqEVU1jHFpMQUqxa3Sxa4hVFqKUh9MhhlwsaD1REKDOOhS1vTFYjagiNGj\nNYFyWq2tGHffOln2cdWmYAwyDzPSI7Tt8qDCzxEYJ6LIFL9iYigCRQY9EApFHkBbmfDNL7TaYigy\n/Pksw/qDSZVdlRop6HRAWTGNBdPH4FePLME/rJoacS35/Bz8Mo5bXsKknB0vRCAQQmVZcgue+trS\nnFkw3ahYzQxWNtdCB2DPyasYcvsRQljMJt3139ncUJaXMPjRg/Pwz2tnptyRq9/pQ3e/J+0LGLWR\n6y4uYvSK+q03NVTAwwYSNp65Yh/G9g8vyD+wymTfKqSBQReLIbeyO5rWE/jefU34xesn0Gl3IRgK\nr5arBVZM8Z1H+p0+9Dt9GFdpgtsbEC1c57MBixg97CrpeGcaHYBH1zeBJIlRtYB8ksXRT3pkucqm\njbdAr9dhz0l16zUpPSGr45MQBz++hpMdvagoMwAFuEum9bqsxw3F4DXDQwiL/4s1FEgnFhONIoNe\nUacxtShi9Nhz6ipOtquXEJZvAjZy3cUeNiBrjjHQJJY0jsW6FXXh7ns0mdBlrWVZq0ypiUFZsV6R\npKbTE8Cf32+PybIOAei0D+PpV45HkgukYhdubwBPPtAMDxuIMVbx2YClxbSoik6uEwLwizdOYe40\n2yiVnPiFihi0XgeC0GHfmW6UlzAYV2nCkMuLQYWLKDFSFVvx+jhc6RlGra0YV+zCpRH5Bl/WNeT2\n4fDH2c32FoN/IhxONuOdh3hMRlq1znFKuWIfLpj7TSl833m5yluJFBotJho3TbRi/ap6GBkKALBj\nb4es3J9sZlkXnEH2BQJ4+tVjSelbn/1MOL5wuceFrbvaseG2qZIJWQ6nFx42MOqHjDdUA8PZVcaJ\nx0CTKDbo4XCysJgNaJxixcmOPtEWiLz0HnC9lEBJkkV4hxaefvuGwrEgNXTcCQBqbgiiRfjzkfKS\n8G+5snkcrCUGMBSJi9eGctYgZwN+Vx6tVa+hHBJAsmmcOgBfvmMqpo63yG7oIKXQuHhGFe6/fWpk\nQ8T6OdgdbtnzU2kxI9k/Op0UnEF++pXjuNKT3CpTKsP4ZFsv7lleJ0sHNbpQ3efncKw1tzN2lzSO\njUjSmYwUduz9FB42cfeXaNeO0iSLeKSat8s+RuqHiGFw2A+K1MGvUrZ+JikrDnetiRfJt5oNWRpR\nbsL/so1TynH7/PFZ25nnOzPrKnBSgWRuNDod8PxfWkTFeaLhjSt0OqxZOgmAsMwxSRAxnkklc5PD\nxeJHfzyScCzpoKAMstPtQ6c9Pe6mgWE24sYQE76YVV+ON/ecjylUDwZDqndTUgtCByybXR256Sot\nRsk+ofFEu3ZIQleQfYPz0RgDwJBbuGvNYI55ZxKx4OZKtF8ZSnt3tJbz/bhrwYSCvIfTDa0Hmqfa\nkjbIfCxYSkyJCwbx+vvt2H+6K6ZmefHMKmx+aD5cbh+KmLAMboALgSTkh9CESCTslC4KyiBf6XGl\nre4uFAJ2Hr6E9asaRBtQhEKhmBtAbq1ytggBuH3++MgKUGltHy8TuHVXG4612rWJLIcQzVbNhSJZ\nmRhoEl+58yYA4QYDu45eRsv5/sgzN2GMCcdVSoByOL3ocXi0ezgJfAGAkJnyTOt10Ol08AWC0EFY\nL0AoqWrb7g7sjusg5/VxeP9YJ0IACJ0ushGyljBorKvAqfbUPZOZTvAqKINcW2lKSRQiER+cuAqS\nDNcix3ceAYCNWw6m58RpoqyYiZm0lQqWNDVUYMfeC1mRycxHUomzKWVmnRUMRcaETxiKzFpsLBkW\nzayKTIRjy4ux4fZpo8JBJzs+UuV5t5gN4X7oOdD9SA6VFgM4LhTOv0jjnCeH0mIKlRZ5oRA+f2RO\nfYXoYira88a7qI+fE897iN41A+HdrZQ0phIyneCVP0+nDMxGGtUV6c2MjV4x8Z1HAKDH4c47MYnZ\ncSUGcjXBeVfRXQsm4Md/PJruYRYMmfSXtF0awNZdbTG7hqYGG27Kge4+YjAUAZ8/CItExm30M8dQ\nJGpsJlWyopsaKmA20pjdYBu1E8tFBlwsrKVhI5htNa5Z9RWK46wXrzlFFz9hzxsVuX8TzUdiLVzV\nWKhkWkazoAwyAHz9b6fjyf88nLbji62Yihj5XYEYmgAr0Qc4E9TYirF+ZX3Ma1J9QqPx+ji0XR7E\nsXNHUm68oBsZi4cNZKVDVKFytdcdU0vLx8T60xyLjabMRMPp9smqhV0xtwZf/NwUuNw+Rb1un/jy\nHGx+6Si6+pXVDZsMJNwsN0ovIJDl/uRy8flD6O7Nvs46SQBnzvfhrwq1BPqGWCwS6cMe9rx9mrLn\nTY2FSqZlNAvOIJcW02l14ZSZGPgCwXCROUUKJhskgvUFMaehAhe7neh3sorCemolnXjZALbt7hiV\nRShXr1utWs0QwvWXdBabgt9InL86JPl3HYAxViNYf2JVo0TIXayVmxncv2oqgLCWcTzxbvdoaL0e\nT/7jPPzg9/sxMJy4MoDnnuV1aBhviTkm6+dwvE0rCVMCF0RSLn5CB9zaNBYMRcTkBTQ1VGDN0sl4\n6j8PyT4WQxGC2gPlJQymT7Lgoxbl7WTLS/ixTEKPw52xXXLBGWS5Ci7J4mYDkX7H08ZboKcI7FFY\nKkHogONtvbCaaSycXgWSBPaektelSK2vJpZFSBIEVi+aiKOf9GQ0KU2tRiAa0gwmMJIhAN39boyr\nNKVskOXS52ThdPtGZYTHC+qI9RhnKBIMTQIKDLLNYhzl5Rp0sXB5czsRU02MjB5FjB4OpxdlJhou\njz9jKm7BEPCzV09EErBWzq2N1MrLDf8ZaAIVZUWwi3Rka2qwYf3KBpAkqSimzFAEnvjyXLxz8CKe\n+s/DkXtv8awarF44Pq1lUOSmTZs2pe3oCXCnQXhBrydw8Gw3PKyyB4uhCIQQEt2t0noduCAQGDEc\nHpbD5R4XLnY7FY+RP4XHFz5GXW0pAlwIQ1koSRl0+bBsdjX0ZLhu743327H1vbakxUtKiqmUlbI0\n0gdF6mQtWPWEDgtmVME57IfXF0i56UIipo0vwxhrrIF84/127Dp6JfIse1gOF64OwcMGMHNyeeR9\nrJ/DOwcuKSovvH3eOJQUx+56gqEQdh66lMK3SB09AVAUAS4DC9QAF8TmB+dhxZxa3LVwIvoGvRlX\nKfOwHD7rcoIgdGiqD7e31esJHBCZw61mBt+9bzZWNo9DgAui5Xw/uLgb2kCTuLUp3OyG0OkwY5IV\nHjaAAacXHhmbDC4YwuCwD3891RVz75275Bh17yVDcbH4bjvPpMcTI9UbuUqk+8m8myrw/LeXYnlT\njeDfb7m5ctTqXU1OtffhsX+Yg+VzalBipNJ2HiH6R2LiwPW6vWTjwqXFFIw50DRDQxydTMs64GJx\n+7xx+MnXbsHPvr4At9xcmdZxmYpi7xupErz4HuODLhYuj/zdMUEgkhAVjYcNZL3s6ZYZVXji/rkZ\nOVcIwLlLAxG3/X2rGmBQuUub3AYQ0b+p1Bw+Z6oNk8eWwlZWJKqqZmT0WLtsSmQnG+BCWDm3Fk/9\n43wsuHmMrPG0fuZIOM50UHAGGQjHQVc216K8xABCF44HrGyuxaaH5mFlcy2s5rBxtZpprGyuxddX\nzwBDkbj38/Ujnwu3VSwvCXed+ZuFE9OacORweuFy+7DhtqmY3VCRtvMIwZc+KalBjp88ebhgCN19\n2U800RDHHwjByCROUuGzS/ms5vtvnyo6WcutQRWDIIAamznmtUQStfwiEhjRrlcQ4wsGgR17Px31\nevj7Jv9dDDQJq5kBoUPSXZZuuWkMbBYjylPoNqbkG/zh/3yCjVsOYuuuNjAUgSWN1bI+V15iEO2O\nF/47g8UzqvBv314amYulxhX/m4rN4XyOi9T9MeAKizhxwSC27mrDxi0H8YMXD+JHfzwCY5EetZXF\nkt+N1hOiAjrx41SbgtzO8H2Lo+uEGYqE0+1DU10Fbp83DlwwNCpJROxzr+5sVWVcDKUTbCoRLbl5\n9oJ0v0614UuflJRtMRSJedMqY5Ixpk+24KNTiTMtx5QZcG0gc5m+GrGUmmg89Y/N+OEfDsPlEdd7\nj88uNTIUljRWC2a+Lm+qRtvlwaTdnU11ozNZ5UjU8jAUidkNFYrihGKCDzodgWQL1KIlaIsMevzi\n9ZORznGELuydkMo41+mACWPMktUOn5s9Fstn1+CZrccFk0jLSwxovqlSkes9Op9k3Yo6nLs0IPpb\nLp9Tg9vnjUOpiYGe1I3E+K8LJMVrpwOIzKl2hxu/2t4i6zcVm4t5Sk0MLCJlU2Wm8CYjXqmrb4jF\n7mOdWDG3BvW1Zdjf0gVWIMzRVF+O9iuDoiVZ6UzwKkiDzMOv7n2BAJ566XDMw1FjM+GJL8+R/BwQ\ndp0lIzhfYzPCy3JwOFmUmRgUF1HoGXBDKC2rSYFRLCmmMCSRvGIxMWist+JYq11ywgUAktBh7bJw\na0m5NchAuBvP7fPH454V9ZGH5ULnoGTpg9lI4Zabx+DuWyfj6VeOpz1WZaBJsD4Oer0OXDCkilZ2\nIdBUX4HSYgNeeORz6Bv04JNLDpy75EDrxUHRtqE8Ygp1/OuvvXcOB85cg88fK23Ycr4X9gHx++oL\niyeNek3KKAmVoqxfWY+OK/IXBULli4MuFmySiYwLbq6MkaAFgM0PzofT7cOVHhdqK02ADnjkVx+J\nHqOmojgSGpO61iRBiC6Omhoq8E9rZ4HjgjjR1ov+IS90MqtOTrT1YvWiiXB7hecXA01i7bIpMZnw\nUkYzGoYiUVtpVvSb8p8TEuVgKBLFRcIGubiIGvk+wh6/U+19+MnXbsHqxROx8T8Owc3GzpOHPrHD\nQAt/j3SXQRW0QeaJNwDBULhs5+lXjmPzg/MlP6tUvQoIr3T/+YuNKDUxGHSx2HnksuDqPbpXJxA2\nimYjhSG3uMHdcNtU/D/vtQnGectMNDY9OA9Ggx4USeJYqx0OCfdKMBSCy+2HkaFk1yADo92ZgLRK\nGqEDnvxKM8pLwzH8Jx9oxtZd7TjZ1guHiwVDEfBzwZSMpg7h615mYjB1QhlIUoez5x1hAYUSBvW1\nZVjWVIN/23YiZ/sBp5txlSasX3U9o768tAhLZhZhycxqydIinkS7lq/cfhPuXdEQEf+3lRWBoUjc\nuWA8vvfbA4LHNNAEqqzCKkiJFgDxY3vygeZRiwIxhHY6Shal8axqHieYfWs2htsAAuGs8drKYsHm\nN6YiPTZ+5XrsONG1ljTYZOxn//vwJXwooxLE4fTiSo9LdL7z+Tm43L5RpWliRlMIftzHz9lHussp\na7vIw/o50YWD2+uHXWJzwy/Gfvu/z4wyxjx8hYmBJuHzh+vVF8+qxuqF4xWNUykFb5ClGk502l2C\n5RbRJPOQWkZcJgxFotTEoEVEdL3YEJt8wBvFPSeFHx5CBzSMK0PzNOG2YzdPtIKmSNmi6vz4eOIf\ncpoSbuYttEo0G2lR1aQamylijIHwZLPhtqm4Z3ldjPRo+CHy4vntpxOOPZ7ND83HzkOX0HrJgQNn\nrsX8rW+IRd/H12AyUvjc7JqclPrU6ZTJTCeqR6+uMIL1BdHv9KKsmMHshgqsX1kvWrKhZFKVei+/\nE4rmV38R/z0Xzxyb9AJA6P3xi4IPjl8R7OAkdA9LLUopEpDK5aH0iYPG23Z3CBrj6gojNj84X/C3\nEbvWcq4N/1m5Mf6IfKjMUEEq8MmFcpMM45HOMWABnU7ye5CETlYjomKDHo/fPwc2ixG11WWw25VX\n1Sih4A2yVMOJYCj8d34FK4SSnSPPtAmWyMOR6MaJd5vdf1sDjp3rEXQ319hMMBvpGMPZP+QFQ5PQ\n6XQ4cKYbrRf74ZZd8nX9wvA7pLXLpkS1YqSxY+8FWTsUIKya9PQrx5MKDQBAbaU5HBsyMZI7+3gq\nywz466mr2Ceg+hPNibZebH5ofuTf/UPejGXVlhhpOD0+UUF9hiKwZslkvLG7I+GxGqdYUV5aJBoz\nNRXpsfnB+QhwIVmGLJ0k6sD2NwsnJjyGksUC/35+UbB+VQNIkpB9D4vtPAMchw9PCIdkDDQBW4Lx\nSSVNsr5gpEORUhJdG9bP4ZTMBhy8fKhSt7IShOK6SrsqsX4OPj8naXBtZUWS36PH4ZHlxnc4WdAj\nMsmZoOANciJXaq1EpiBP/ENaZmLgZgOCu0cDTWL9quuSlEqSU4DwyvcXDy/Cj/90DFftwyOdTGIN\nW/Tq+LWd52IMkRLVHK8viP4hLz440SkqvqBkh0Lr9aPiZnLLxaJFIJQYYwB48G+mYcvbnyR8H5/N\nLifJRAk3jSvDJ5cHRP9uMTHY9OA8XOlx4bk3Tgq+x+sLJlTR4vn6384AQxHQIYQ9J7ti6jBrKoz4\n4QPNIAkCJIGMieKLkagDW1fvsKIMaaUks8sWej8XDKLjypCgTv4iiV0+j5ys8XT8VolCbjpduEd2\n9CJFSahACYlK2RJ1VYoXimFEsv6njS9L+D3c3oAsRUdNy1plErlSow2GWBxN6CF9c895wdXXksax\nMDLXa4mVJqcAYcP244dukWXYWi8J18vJobyEwa5jV2J2WkIrVqU7lOi4mVyS7V1K63UwGmhZcf7o\nhytRkolcCAL42t9Nx0/+dER0MTR7ZOcxuaZUspvQhauDGGMx4JpDPAt9wfTKSAzv/tum4UvL63HV\n7oLT7cek6pK01ssng+SCmJC3IE6W+OdZ6S47+v0kQeCpf5yHrbvaceKcHQPDPlgVxD+VLszVQuq8\nVjOD79wzKxLr51G6iJFLqouS+Dkiui+yz8+BpkgAIew7043WS47IxkLoe0jZhWg0Les0kMiVqkSi\nj79hlKwik11xJjJsySScRdM4pVw0vp3JPqBSK+dEsdI50yphKyuSFecXeriEfpvGOivarwziqn04\n4Qr61qYalJkYzJkqHNcfV2mKNPFgKBLTJlgFBfWBsHuseapN1CAbaBL33zY15jWGIjGpulR6kFlE\nauKbWJWeBYTc51kpQrkPcp+PZBbmaiB13jlTbai1iS+IlC5iEpHKokRqjjAyesyur8DBs9dzR+I3\nFkLfI94uAOH6cS54Xcs6Va+AUm4Ig5zIlZpMXEPJKjJdK06pG9xAkyg26EdiIAT8gWCkBpIvR1kx\np1Y0+zITfUD5HYwvEBRdWEjZQ5rS4f5VDQnj/FIPl5j3Qyj5Jubceh2WzKrGfZ8PG9v4uH6piUZT\nfUU4hhllBNavqsfxNrtguKPMxEi6rRdOHxPjfckXxBbEz/3zUgwOqi8ko0acUopkDVW6XMG5et54\nUlmUJMrFOXdROGQktbEQsgs0RWY17+KGMMg8QjvOVOMaamWnKoU3Zo11woII0SIF0VnM0eUobILE\niERuNDmlMkLE72AsZhoMLZzRLRXnWTzjenhAeKdbHiNaLwX/20jdD6XFFP7H381AsUEPm8WYlJsv\nLLAxVnBSmjbBggMSiWkrm8dJfodcRWxBTNPqTz+pPs/pJF0L81w9rxDJLg6kNh/htrfJu8Lj7UI2\n8y5uKIMsRLaSLZJFyJiNqzSB9XPoHfCMEhCIz2KOJtkVa6ouwfgdjFQimpi7M76mVq1JR+p+cLr9\nsJgZyftBzqJLbFJas3QSzl1yCE465SUGWEtG6y/nE8nkFiglH55ntV3BuX7eaJJ9TiXnqvoKtJzv\ny3h8Ph3ckAY5emeXrWSLZBEyZv1OH+5aNBGfm1ml2BAls2JNxSUoVzPbYqIxd1ol7r51MrZ/GC69\nklNTm+qkk4n7QWpSykacsZDIt+f5RiWZ51RaDEU4KTTfnpsbyiCL7exm1Vdg97HRbt9c+zGljNnR\nT65h9cIJiserdMWaqktQbiJacREVMe5i40vWZS5FJpNvhCalXIn3pYNkyuGUkq3kKY30IzVXFcpz\nc0MZZLGd3efn1mBlc23O/5hSxqx3wJOSO07uijVVl6Bc5bOrvcMxKmrR40tXFi1PNh/uXIr3qYUv\nEBBM6nr+X5al5XyFMjlrCCM0VxXKc3PDGGSpnd3JEbHxXP8xpYxZRVlRRtxxqboE5SqfSamopTuL\nNvrhJmkKnM+f8fshF+J9aiGmJf+vv96LjV9uVv18hTI5aygn35+bguyHLIScnR3/Y+bqw8sbMyEW\nzBBWC2L9HHocbtWaakuNQa5LkO91ajGJuy3FVNSUNK5PFYYiMbaiOGfvh3xASjrzs+4hON3yleWU\nkuvPs4ZGPDfMDrlQkj3E3HEPrp6O/v7rtbNibt01SyfD5faltGtI1SUYvYP50ctH0NXvHvWeeBU1\nnnzIoo0nHbHufEFSSz6YWEteI/3cyPdnrnHDGORCSfYQc8eRccr0Ym7dj1qugvUFU4q7quUSZCgS\nTz3YrKghRT4trNId684HsimdqSGNdn/mHjeMQQYKK9lDKlYi5dbl9V/ViLuqEa9R2pAinxZW6Y51\n5wPZkM7UkId2f+YeN5RBvlGSPZRoXGdbvYhHiWhEPiysclkxKtNkWjpTIzHa/Zmb3FAGmSffM/ES\nIbe0CMjduKsU+bCwysdYd7rIpHSmhjy0+zM30QIFBYhUJnQ8uRZ3VUIuZ9HyiyIh8vmapwLvBdHc\n1NlHuz9zE80gFyh8aVF5iQGELtzhSYhci7sWCmqUh2lopAvt/sxNNJ9RgRLv1jUZKezY+2lOx10L\njXyIdWvcuGj3Z+6hC4VCCVqwpw+73ZmtUxccNptZ1vXUag6VIfe6SqFdc2HUuLYao1F6XbX7Ux5q\n3a82m1n0b6rvkH/605/i1KlT0Ol0ePzxx9HY2Kj2KTRSoNAT2nIR7Zpr5DLa/Zk7qGqQDx8+jIsX\nL2Lbtm04f/48Hn/8cWzbtk3NU2hoaGhoaBQkqiZ1HThwACtXrgQATJkyBYODg3C5hHVsNTQ0NDQ0\nNK6jqkHu7e2FxWKJ/N9qtcJuT9yMXkNDQ0ND40YnrVnWifLFLBYj9HotiUAtpJIFNJJHu67pQ7u2\n6UG7rukh3ddVVYNcWVmJ3t7eyP97enpgs4kLVDgco7v8aCSHlrGaHrTrmj60a5setOuaHjKRZa2q\ny6VGTu8AAAVHSURBVHrx4sXYuXMnAODs2bOorKyEyaR1c9HQ0NDQ0EiEqjvkOXPmYPr06bj33nuh\n0+nw1FNPqXl4DQ0NDQ2NgkX1GPL3vvc9tQ+poaGhoaFR8GRVqUtDQ0NDQ0MjjNZcQkNDQ0NDIwfQ\nDLKGhoaGhkYOoBlkDQ0NDQ2NHEAzyBoaGhoaGjmAZpA1NDQ0NDRyAM0ga2hoaGho5ABp1bLWSA8e\njwff//730dfXB5Zl8c1vfhPLly8HAOzduxdf/epXce7cuSyPMv8Quq5LlizB97//fVy8eBHFxcV4\n4YUXUFpamu2h5hVC19VkMuGXv/wl9Ho9jEYjnn32We26JonX68UXvvAFfPOb38TChQvx6KOPguM4\n2Gw2PPfcc6BpOttDzFvir+0PfvADBAIB6PV6PPfcc5LS0Mmg7ZDzkA8++AAzZszAa6+9hueffx4/\n//nPAQAsy+I//uM/VL9JbhSEruuf//xnWCwWbN++HXfddReOHj2a7WHmHULX9Wc/+xmefvppvPrq\nq2hqatL6pqfA7373u8hi5oUXXsD69euxdetWTJgwAdu3b8/y6PKb6Gv7/PPP45577sFrr72GVatW\n4eWXX1b9fNoOOQ+56667Iv/u6urCmDFjAAC///3vsX79ejz33HPZGlpeI3RdP/jgA3z7298GAKxb\nty5bQ8trhK4rRVEYGBgAAAwODmLy5MnZGl5ec/78eXR0dODWW28FABw6dAibN28GACxfvhwvvfQS\n1q9fn8UR5i/x1/app54CwzAAAIvFgrNnz6p+Ts0g5zH33nsvuru78fvf/x6ffvopWltb8cgjj2gG\nOUWir+u//Mu/4K9//Suee+45VFRU4KmnnkJZWVm2h5iXRF9XiqJw//33o6SkBKWlpfjud7+b7eHl\nJc888wx++MMfYseOHQDC4QHeRV1eXq71o0+B+GtrNBoBABzHYevWrXj44YdVP6dmkPOYN954A598\n8gn+9V//FWPHjsXGjRuzPaSCIPq6BoNBTJo0Cd/61rfw7//+73jxxRfx2GOPZXuIeUn0dbVarfjN\nb36DuXPn4plnnsHWrVvx5S9/OdtDzCt27NiB2bNnY9y4cYJ/11SRk0fs2nIch0cffRQLFizAwoUL\nVT+vZpDzkDNnzqC8vBxjx47FTTfdhOHhYXR0dEQae/T09OD+++/Ha6+9luWR5hfx15XjOBAEgXnz\n5gEAlixZgl//+tdZHmX+IXRdDx06hLlz5wIAFi1ahLfffjvLo8w/PvzwQ1y+fBkffvghuru7QdM0\njEYjvF4vDAYDrl27hsrKymwPMy8RurZVVVXYsWMHJkyYgG9961tpOa9mkPOQo0ePorOzE0888QR6\ne3sRDAaxe/duEEQ4R2/FihWaMU6C+Ovqdrtx7733Yu/evVi7di3Onj2LSZMmZXuYeYfQda2vr0dH\nRwfq6upw+vRpTJgwIdvDzDuef/75yL9//etfo6amBidOnMDOnTvxd3/3d3j33XexdOnSLI4wfxG6\ntr29vaAoKpJTkg60bk95iNfrxRNPPIGuri54vV5861vfwooVKyJ/X7FiBXbv3p3FEeYnQtd14cKF\neOyxx2C322E0GvHMM8+goqIi20PNK4Sua1lZGZ599llQFIXS0lL89Kc/RUlJSbaHmrfwRmPJkiV4\n7LHHwLIsqqur8bOf/QwURWV7eHkNf23//Oc/g2VZmEwmAMCUKVOwadMmVc+lGWQNDQ0NDY0c4P9v\n3w4JAAAAAAT9f+0NA5ww6UMGgAFBBoABQQaAAUEGgAFBBoABQQaAAUEGgAFBBoCBAGG9EFREz6l5\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6N0p91k2iFCP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Try creating some synthetic features that do a better job with latitude.**\n",
+        "\n",
+        "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n",
+        "\n",
+        "Or you could break the space into 10 different buckets.  `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n",
+        "\n",
+        "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n",
+        "\n",
+        "What's the best validation performance you can get?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wduJ2B28yMFl",
+        "colab_type": "code",
+        "cellView": "form",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def select_and_transform_features(source_df):\n",
+        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "  return selected_examples\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "UmWxto9MJe-1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "5af4f108-941a-483a-8a00-11a0f55c78ea"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 226.45\n",
+            "  period 01 : 216.27\n",
+            "  period 02 : 206.16\n",
+            "  period 03 : 196.16\n",
+            "  period 04 : 186.29\n",
+            "  period 05 : 176.56\n",
+            "  period 06 : 166.98\n",
+            "  period 07 : 157.59\n",
+            "  period 08 : 148.42\n",
+            "  period 09 : 139.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Ym1pA2ulSfn1jf8YN6ma3V+XmwBmMjxnGin3vkVyQyuN7n2NR3FwmRo6+Jntj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\n839hHjpYMHQAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pZa8miwu6_tQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PzABdyjq7IZU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n",
+        "\n",
+        "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xdVF8siZ7Lup",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def select_and_transform_features(source_df):\n",
+        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "  return selected_examples\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U4iAdY6t7Pkh",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb
new file mode 100644
index 0000000..2b8d4d2
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,1875 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "first_steps_with_tensor_flow.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "ajVM7rkoYXeL",
+        "ci1ISxxrZ7v0"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/first_steps_with_tensor_flow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# First Steps with TensorFlow"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Bd2Zkk1LE2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Learn fundamental TensorFlow concepts\n",
+        "  * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n",
+        "  * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n",
+        "  * Improve the accuracy of a model by tuning its hyperparameters"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MxiIKhP4E2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6TjLjL9IU80G",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "In this first cell, we'll load the necessary libraries."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rVFf5asKE2Zt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ipRyUHjhU80Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll load our data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9ivCDWnwE2Zx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vVk_qlG6U80j",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r0eVyguIU80m",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 402
+        },
+        "outputId": "e798ba1e-48de-41ba-e499-f9bc64975d0b"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>15334</th>\n",
+              "      <td>-122.3</td>\n",
+              "      <td>38.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>144.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>162.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10407</th>\n",
+              "      <td>-120.2</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>1214.0</td>\n",
+              "      <td>228.0</td>\n",
+              "      <td>633.0</td>\n",
+              "      <td>199.0</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>148.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16623</th>\n",
+              "      <td>-122.7</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>188.0</td>\n",
+              "      <td>62.0</td>\n",
+              "      <td>301.0</td>\n",
+              "      <td>72.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>129.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4173</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1138.0</td>\n",
+              "      <td>228.0</td>\n",
+              "      <td>725.0</td>\n",
+              "      <td>219.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>187.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13792</th>\n",
+              "      <td>-122.0</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>726.0</td>\n",
+              "      <td>204.0</td>\n",
+              "      <td>538.0</td>\n",
+              "      <td>203.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>230.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12884</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>39.2</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>3819.0</td>\n",
+              "      <td>702.0</td>\n",
+              "      <td>1983.0</td>\n",
+              "      <td>658.0</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>72.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2322</th>\n",
+              "      <td>-117.5</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1566.0</td>\n",
+              "      <td>294.0</td>\n",
+              "      <td>1056.0</td>\n",
+              "      <td>279.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>105.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16519</th>\n",
+              "      <td>-122.7</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1059.0</td>\n",
+              "      <td>150.0</td>\n",
+              "      <td>400.0</td>\n",
+              "      <td>154.0</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>343.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5045</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>2337.0</td>\n",
+              "      <td>352.0</td>\n",
+              "      <td>981.0</td>\n",
+              "      <td>328.0</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>490.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11961</th>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>2176.0</td>\n",
+              "      <td>460.0</td>\n",
+              "      <td>1067.0</td>\n",
+              "      <td>357.0</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>78.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "15334     -122.3      38.3                52.0        144.0            54.0   \n",
+              "10407     -120.2      38.0                11.0       1214.0           228.0   \n",
+              "16623     -122.7      38.4                52.0        188.0            62.0   \n",
+              "4173      -118.0      33.9                36.0       1138.0           228.0   \n",
+              "13792     -122.0      37.4                32.0        726.0           204.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "12884     -121.8      39.2                25.0       3819.0           702.0   \n",
+              "2322      -117.5      33.9                35.0       1566.0           294.0   \n",
+              "16519     -122.7      38.4                21.0       1059.0           150.0   \n",
+              "5045      -118.1      34.1                52.0       2337.0           352.0   \n",
+              "11961     -121.4      38.7                37.0       2176.0           460.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "15334        89.0        48.0            1.0               162.5  \n",
+              "10407       633.0       199.0            3.1               148.6  \n",
+              "16623       301.0        72.0            0.9               129.2  \n",
+              "4173        725.0       219.0            3.4               187.2  \n",
+              "13792       538.0       203.0            4.5               230.4  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "12884      1983.0       658.0            2.4                72.5  \n",
+              "2322       1056.0       279.0            3.5               105.4  \n",
+              "16519       400.0       154.0            6.9               343.1  \n",
+              "5045        981.0       328.0            5.9               490.4  \n",
+              "11961      1067.0       357.0            2.4                78.4  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HzzlSs3PtTmt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Examine the Data\n",
+        "\n",
+        "It's a good idea to get to know your data a little bit before you work with it.\n",
+        "\n",
+        "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gzb10yoVrydW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "e3b20757-9906-4857-9a67-49a07375d4e4"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "eb50cfde-c9f3-477e-cc8e-60078d36a042"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an input function for predictions.\n",
+        "# Note: Since we're making just one prediction for each example, we don't \n",
+        "# need to repeat or shuffle the data here.\n",
+        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+        "\n",
+        "# Call predict() on the linear_regressor to make predictions.\n",
+        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "\n",
+        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "\n",
+        "# Print Mean Squared Error and Root Mean Squared Error.\n",
+        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Squared Error (on training data): 56367.025\n",
+            "Root Mean Squared Error (on training data): 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AKWstXXPzOVz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Is this a good model? How would you judge how large this error is?\n",
+        "\n",
+        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+        "\n",
+        "Let's compare the RMSE to the difference of the min and max of our targets:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7UwqGbbxP53O",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "f7c2affc-205a-4c3f-c306-0ed4eee4a3cc"
+      },
+      "cell_type": "code",
+      "source": [
+        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
+        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
+        "min_max_difference = max_house_value - min_house_value\n",
+        "\n",
+        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
+        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
+        "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
+        "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Min. Median House Value: 14.999\n",
+            "Max. Median House Value: 500.001\n",
+            "Difference between Min. and Max.: 485.002\n",
+            "Root Mean Squared Error: 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JigJr0C7Pzit",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our error spans nearly half the range of the target values. Can we do better?\n",
+        "\n",
+        "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
+        "\n",
+        "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "941nclxbzqGH",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "5df7a88e-d729-4445-cd29-8b8124e7d90c"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = pd.DataFrame()\n",
+        "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "calibration_data.describe()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.9</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.1    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            0.1    119.4\n",
+              "50%            0.1    180.4\n",
+              "75%            0.2    265.0\n",
+              "max            1.9    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "E2-bf8Hq36y8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
+        "\n",
+        "We can also visualize the data and the line we've learned.  Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
+        "\n",
+        "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SGRIi3mAU81H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "sample = california_housing_dataframe.sample(n=300)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "N-JwuJBKU81J",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7G12E76-339G",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 361
+        },
+        "outputId": "5738bf5c-2b08-47f3-939b-0989090c38b9"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Get the min and max total_rooms values.\n",
+        "x_0 = sample[\"total_rooms\"].min()\n",
+        "x_1 = sample[\"total_rooms\"].max()\n",
+        "\n",
+        "# Retrieve the final weight and bias generated during training.\n",
+        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
+        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "# Get the predicted median_house_values for the min and max total_rooms values.\n",
+        "y_0 = weight * x_0 + bias \n",
+        "y_1 = weight * x_1 + bias\n",
+        "\n",
+        "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
+        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
+        "\n",
+        "# Label the graph axes.\n",
+        "plt.ylabel(\"median_house_value\")\n",
+        "plt.xlabel(\"total_rooms\")\n",
+        "\n",
+        "# Plot a scatter plot from our data sample.\n",
+        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
+        "\n",
+        "# Display graph.\n",
+        "plt.show()"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AgpTKzg/nFQhJJ6+gYWsLtjWdQlBk42c4uCAiovzHACALqbFFTWpZISyd4IKIiHILlwCy\nlNJb1KSWFcJyYf87t0USESmDAUCWUjKvwOsPwDcQRKnNjE63L+52vQ5YMGdsVu9/z1S5ZSIirWAA\nkOXS2aIW22lazMIBxILZY3DHdVPTaabq1NwWSUSkRRw65bFwp9nR40UIgMc3uH3QajZEtit+df7F\nWLFYnQ5UqdMReXIfEZHyOAOQpHTWoDO5fi3VaY6wGvHY7TVwlBZi3JgSxQ/VUHq6nif3EREpjwGA\nTOl0asOxfi3daXphNhlUC0KUnq5Xq9wyEZGWcQlAptjp9GSq56Xz2FSpVUsgETWm63lyHxGR8hgA\nyJBOpzZc69fD1WnKma5PJTdA6XLLRERaxyUAGdJZgx7O9evhOO5Werregs17j+PAsY6kl0LS2RYZ\nnXtBRESDGADIkM4atBrr13KTCYfjuFup0xELrSZsa7pwDkEquQHJbIsUyr24atZYLLlyAmsHEJHm\nMQCQwWIyoLqyHNsaW+NuSzSdruRxwakmEwp1muEgwjayQPbrRz9OKpgQmnmorizD/qPiSyFqHEIk\nlIy4acdn6Ov3sXYAEWkeA4AEwp1uuPPS64BgCCiL6nwTUWoqXiy7PhAI4vq5E2SN8GODCEdpAaon\nlyUMIpIJPoRmHrp7vdguEEAB6iyFJMq94KmHRKR1DAASiO10wyfpVU8ukz2KTGYqXmyELdWh/fmT\nU9jedErWjEDs9bS5+mVNw6eytS965iHTW/lYO4CISBoDAAlSne6BY53w+gNJjSKl1q8TjbClOrRw\nUJKoU051VKzEaFrJpRA5WDuAiEgaM6EkyBlFKiVRrYACixEji8yynktse2Gq16PU+yC1lU+pssFh\nrB1ARCSNMwASMjWKlBphNx5xIhAM4UBLO7p640/yEyI2xZ3q9Sj1PggthRgNOtWqJArlXlw1awyW\nXDkhreclIsoHDAAkqDFtLbTGLzXC7nR7BXcfABcSEmOJdcqpXo/S70P0Usj6Lc2qnfInFHCocfYB\nEVEuYgCQgFIZ/FJr/COLLLCYDZHT+uQoLbJgZqUdf/nkdNxtUp1y7PWUl1zYBRAtNlBRo6hQpjL1\n0zlSmYgoXyUVADQ3N+P48eOoq6tDT08PiouL1WpX1lCqmI5UFv2tCyYDEBjKS+g+58X1l02A2WhI\nqlOOvZ7JE8vg7u4HMNghd/Z4sGXfSRxoaY8LVJQuKqRUpn4mT1kkIsoXsgOAV199FX/84x/h8/lQ\nV1eHn//85yguLsa9996rZvuyRjqjyEQj3aurR8PjCyb1nKU2K+zF1pQ75fD1WM1GdEXNTsSu88dO\nySs5mk43t2A4TlkkIsoXsr8l//jHP+K3v/0tRo4cCQB4+OGHsX37drXalVcSjXSh06FM5OQ+q1m4\nQ4+e5g93yrF1A+Rm1UfvQBAj59CjZLP4083UH45TFomI8oXsGYARI0ZAHzWq0uv1Q34mcYlGuo6S\nAtEku3kzR0Gv08me5k92VOzxDYjOTkQTm5JPdxSeam4BK/0REaVHdgAwYcIE/Md//Ad6enrw3nvv\n4e2338bkyZPVbFvekJNFL9URGvR62dP8yVbsc/WIz05EE5uST6VCYLRUcyxY6Y+IKD2yA4CnnnoK\nr7/+Oi666CJs2rQJl156Kb7xjW+o2ba8kmikm6gjTLT27vUH4OzqR+ORNsHbxUbFpcXisxPRhKbk\nlRyFJ5tbwEp/RETpkR0AGAwG3HXXXbjrrrvUbE/ekjvSTbYjjJ2CF9tLIDYqtpqNorMTwGC1PrEp\n+WRG4Upn6me6tDARUb6RHQBccskl0Ol0kZ91Oh1sNhv27NmjSsPyldwOXm6HGTsFL0ZqVCx4fO9k\nO+pqx8NebBV9fTmjcDUz9dWoTUBEpBWyA4DDhw9H/tvn82HXrl04cuSIKo3SsmQ6TKkp+FhSo+JU\n1+HljMIzXemPI38iInlSGoKZzWYsWLAAO3fuVLo9mpfM1japKfhoVrMewVAIgaB0rQGh7YSJJDrg\nRypHQMmDf5JtNxGR1smeAdiwYcOQn8+cOYOzZ88q3iAtSzapTmoKPprHF8TWfa3Q63Rpj7pjSY3C\nO7r7mKlPRJSlZAcA+/btG/JzUVERXnzxRcUbpGXJbm2TmoIXoub+eKHcBmbqExFlL9kBwHPPPadm\nOwipdZixiXAjR1jg6h3+UXc4ibG6slzwNENm6hMRDa+EAcCCBQuGZP/HYjlg5aSytS12Cr7AYsSz\nr36UMIgId9C2kQWKXkNsEmOpzYzxFUXo8/jhcnuZqZ8leIASJYN/L/kpYQCwfv160dt6enoUbQwN\njugDgSCajraju9cHe8w+fLF/iNFT8FJBhNGgw/otzZEO2lF64ThgJQ7Qid2W2On2odPtw8I5Y3D9\n3An8AhlmPECJksG/l/yWMAAYO3Zs5L9bWlrgcrkADG4FXLNmDd555x31Wqcx4X9sB451oLvXh5Ii\nC6oryyKdf3THLfUPUWp/fGwH3ebqV2xbnlQS44FjnVi2aAo7/2GWbulm0hb+veQ32TkAa9aswc6d\nO9He3o4JEybgxIkTuPvuu9Vsm+bE/mNz9XqxrbEVBv3gEozcf4himflqH6DD+vzZjQcoUTL495L/\nZM/hHDx4EO+88w6mTZuGt956C+vWrUN/f7+abdMU6X9sTska/2L76WP3x8vpoNMRTmIUwqz/4af2\n50/5hX8v+U92AGA2mwEAfr8foVAIM2bMQGNjo2oN0xqpf2ydbi863T7B25L5h6h2Bx1OYhTCrP/h\nxwCNksG/l/wnOwCYNGkS3nzzTdTW1uKuu+7C6tWr4Xa71Wybpkj9Y7PbLLDbzIK3JfMPMRMdtFRl\nwGzh9QfQ5upTrBJhrmCARsng30v+k50D8Oyzz6KrqwvFxcX44x//iM7OTtxzzz2i9+/v78ejjz6K\njo4OeL1e3HvvvZg2bRoefvhhBAIBOBwOrF27FmazGZs2bcJrr70GvV6PZcuWYenSpYpcXC4xGnQo\ntJoEt++F/xEqcfJdbIJgecmFXQBKyOb6/IFgEC9tPIid+1s1m9HMA5QoGfx7yW+6UCgkdoLsEMuW\nLcPNN9+Mv/u7v0NJSUnC+7/99ttobW3FqlWr0Nrairvvvhs1NTW4+uqrceONN+KFF17AqFGjUF9f\nj1tuuQUbNmyAyWTCbbfdhl//+teSr+F0qjPz4HDYVHvuRGIPzQkbX1GEp+6sBYDz23Ha0en2oGSE\nBbOryrGibkpKnVd4O+HkiWVwd2sjl0PsPa6rHZfXGc1Cf9f5vK97OP8dDxe1rznb/l60+hkrTXbP\n8cgjj+Dzzz/HLbfcgu9+97t499134fMJr0sDwE033YRVq1YBAE6fPo2LLroIe/bswbXXXgsAWLhw\nIXbt2oX9+/dj5syZsNlssFqtqKmp0VxugVQCYJ9nAAOBEAx6PZYvqkT1ZDtGjjDD1evFgZZ2NGxt\nSXjIj5BwgqDVLHsSSLTtuTCdnqmDiXIFD1CiZPDvJT/J/va/9NJLcemll+Lxxx/H3r17sWnTJjzz\nzDPYvXu35OO+9rWv4cyZM/jlL3+Ju+66K5JMWFZWBqfTifb2dtjt9sj97XY7nE7pI25LSwthNKrz\nh6hGlJXI6fZz6HSLZ9sazCY4ykfgpY0Hsa3pVOS28FbAwgIzVtXPTPn1U7nmQCCIdX/4FLsPnYaz\nqx+OkgJcMWM07l4yHQZD9k2ny32Pc43HNwBXjxelxRbJYG44/q6Hk9auF9DeNWvtetWQ1PCvp6cH\nW7ZswbvvvosTJ05g+fLlCR/zm9/8Bn/729/wf/7P/0H0aoPYyoOcFQmXq09+o5OQqWml2Om0gD8A\nu038DICAz4+Tp7qwc398TX0A2Ln/FK6dMwb93oGkp+gSXbPY1F/sdHqbqx+bdnyGvn7fsEynJ5qi\nlPMeh9+HbJvuFJJMhTatTZdq7XoB7V2z1q4XUCfgkR0ArFy5EkePHsXixYvxne98BzU1NZL3P3To\nEMrKyjB69Gh8+ctfRiAQwIgRI+DxeGC1WnH27FlUVFSgoqIC7e3tkce1tbVh9uzZqV9RFpP60k50\nBkCbS/xo3Y4eD55Z9xG6epVLbBOq6T/tS3asWDyYc5AtBUKE3tPqyWWoqx0Pe7E10g455yzkUtlT\nVmgjonTJDgC++c1v4itf+QoMhvgv9pdeeimy3h/28ccfo7W1FY8//jja29vR19eH+fPnY/Pmzbj5\n5pvx3nvvYf78+Zg1axaeeOIJ9PT0wGAwoLGxEY899lj6V5aFpL60E2XbSp0UCCByAmD4OQPBEO64\nbqpibe10+/DhoTNobHbi0iqHaDs6ezxwuvowriIz03NC7+m2plPY1nQKZTEd+PJFlSgsMGPn/lOC\n73GudKqs0EZESpAdACxYsED0th07dsQFAF/72tfw+OOPY8WKFfB4PHjqqacwY8YMPPLII2hoaMCY\nMWNQX18Pk8mEhx56CCtXroROp8N9990Hmy3/1nbkfGlLbZ+TGsEK+XNTKxAKYcXiqqRHr1Jt9fgC\n2HnoDKxmPTy++OTDEIB/23AgIyNnqXYC8R24Qa/HqvqZuHHu+Lj3OJc6VZZcJiIlpJcCfp7Qur3V\nasVPfvKTuN+/8sorcb+74YYbcMMNNyjRlKwl90s7+lS/WLGzBMUjzOjqFd6JEQwB25pOwWDQJz16\nlWrrBeJHRGdq5CyvnfEduNB7nCudaiAYxOa9x6HTAULpMqzQRkRyKTI80+nEOwMapERZzXCRnTWr\nLscPv30FVt89F2UizxmWyhY3qbaG+fwBjLZLd4hqb6+T005AXrlkNcueKrlVsmFrC7Y1nUJQJFeW\nFdqISK7symzKY0qW1QyPYG2FZlRPLpO8byqHdki1NazUZoHXP6D4aydDTjsBeR24GmVPA8Eg1m9p\nxhMv7cb3/3M3nnhpN9ZvaU6pbgMgvUyh1wELa8ayQhsRyabIEgDJo2RZzXDG+oFjHZL3k9P5CW17\nC7fpgwOn4fHFj1ynTSjFh4fOpP3a6Yp+Tzt6PIL3kduBK132VOmkQqllilAIuP6y8Vm3W4GIspci\nAcDEiROVeJq8p2Sd/NjORYxU55eoNv6KuirUz78Y//2nZhw+7oLL7Y10ivXzL8bh4y7R3QCJXlsp\n0e9pZ48HW/adxIGWjpQ6cCU/HzWSCqV2gtiLufZPRMmRHQC0trbi+eefh8vlwhtvvIHf/va3mDt3\nLiZOnIhnn31WzTZmVCaKwEgl+km1qcBiRL93AAUWo+RUcDCEIVvgxMgZoRZajFj595cIvi9iuxKs\nZgO+Uj066ZFzOu+9xWTA6LIRuOO6qfAuTO8zTPbzEaJGUqGcWgZERHLJDgCefPJJfOMb34hk8U+a\nNAlPPvkk3njjDdUal0nZeFJcdGGajh5vpHMfOcKE7nN+wceEk8MSVVRMdoQq1CnGT5lbMG1CKb6+\nuAqFFvmTS0oX4FGiA0+X1Gg9naURns5GREqR/S3t9/tx7bXX4tVXXwUAXHbZZWq1aVhkYxGY2DaF\nO3exzj9ap9sn2X4lRqhKTZln43ufLrVG69l83DIR5Zakhlc9PT2RLX9Hjx6F16tehncmZeNJcYmK\n3Mgl1H6vPwCfP6DYtrfoEXey292y8b1XyvJFlairHYeyYiv0OqCs2Iq62nGKjNZ5OhsRpUv2DMB9\n992HZcuWwel0YsmSJXC5XFi7dq2abcuYbCwCI7fITWmRBV3nvIJFYYCh7Y+dareYheO/ZEeo6Uzh\nZ+N7rxSO1okom8kOAK644gps3LgRzc3NMJvNmDRpEiyW/Mg6Vmu9Vq02hZUVW/HUnbXoPufDi7/9\nBJ3u+KqA0e2PnWoPl/K1mg3w+QMpryenM4Wfje+90rIhJ4GIKJbsJYBDhw5h165dqK6uxjvvvINv\nf/vb+Pjjj9VsW8aoUQQmXXKK3MypKoet0IxxjiLUTK0QvY/FZJCcareYDHjsm5dizarLIzXz5fD6\nAzjZ5k5rCj8b33siIi2QPQOwZs0a/OhHP8LHH3+MgwcP4sknn8Szzz6L119/Xc32ZUyik+IyKbwd\nrn7+xQAQtwvAbrOgZurQLX718yehzzOAw1+40NXrjWu/1FR79zkf/uOtg6idViFr2j52d4IYuVP4\nzGwnIso82QGAxWLBxIkT0dDQgGXLlqGyshL6PKo6JnVSXKaIraWvXjkXvX3+SB2A6LYJPebK6aPi\ntuIlWlLo6h3cNRAKhfCNxdJlzme7AAAgAElEQVTHCMstQpTsGQdcKyciyhzZPXh/fz/eeecdbNmy\nBV/5ylfQ1dWFnp4eNds2LIYzuzrcsXb0eBHChbX0jTs+j9T+j22b0GN2HjqDjTs+G/Lccuvm7zx4\nRnLaPpndCamecSDnMUoesENEpEWyZwD+5V/+Ba+//jr++Z//GUVFRfj3f/933HnnnSo2TVtSKR2b\n7GPCU+of/a0N3eeEjxH2+AJwdvVjnKNI8PZEuxN0OsAucwo/lcp/ShcNIiLSKtkBwNy5czF37lwA\nQDAYxH333adao7Qole1wTlef6JS+0GPCU+1XTL8Ia17bJ94YiSqCBRYjRhaZ0dUbH0DYbRY8uGwW\nHCUFkh16Op14PhYNIiIaDrIDgEsuuSRSBAgAdDodbDYb9uzZo0rDtCaZ7XDRHagYqfX3seVFsJr1\nkW2A0axmAxwCSXvRrynU+QNAzVSH6MxBtFQ7cTUO2CEi0irZAcDhw4cj/+33+/Hhhx/iyJEjqjRK\ni5IpHSsnCU9q/d1iMmDezNHYuq817rZ5M0cJPk7qNcuK5U/5O7v60XikTfD2RJ14PhcNIiLKtJSO\nAzaZTFiwYAHWrVuHb3/720q3SbPkbIdLlIQntEVQyNevnQK9Tof9Le1o7xo8yEfscVKvWVJkxndv\nmY6x5UWi0/exU/5iCwyJOnEtFA0iIsoU2QHAhg0bhvx85swZnD17VvEGaZmc7XCJkvD+6baZ+NJF\nxbJf655bZ+HY/3ZIJuJJvWZXrw9rXts35Pjh2EBAqW2DPA6XiEg5sgOAffuGJo0VFRXhxRdfVLxB\n2Sqds+qTJVU6NtF+/r98cgp3XC8eAMReh9VsTDhtLqcssdg6fjLbBmdPKUv43rJoEBGRMmQHAM89\n9xwAoKurCzqdDiNHjlStUdkk27adWUwGVFeWY1tj/Po9AOxv6cDCmt64THyx67h/2RxZryk28o4V\nu44v91AjAKJLA9FSKRoUHfQQEdEg2QFAY2MjHn74YZw7dw6hUAglJSVYu3YtZs6cqWb7hl02bjur\nu3ScaADQ6fbi6Zf3xgUqYtdRWGBG/VUTE75m9Mi70+2RdfogIG/2IGz/0Q4svSYga4ZFzgE7QkHP\nVbPGYsmVE1gzgIg0T/a34E9+8hP8/Oc/x65du7B792688MIL+NGPfqRm24Zdtp5Vby+2wm4zi94e\nXUWwYWuL5HXsPnRa1nWER95rVl2O1XfPFX392HV8uRUIgQvBg1KEqiRu2vEZGra2KPYaRES5SnYA\noNfrUVV1YcR7ySWXwGDI76QrOdvO0iFVzlbstkAwiLf+fAx9XnnBR1NzO5xd/aLX0d7Vn9R1WEwG\nWacPRlu+qBJ1teNQVmyFTgfodYIPVTSTP1uDNyKibCF7CUCv1+O9997DvHnzAAB/+ctf8j4ASHXb\nWaKEQam8AgCSOQdyM+rDXG4PEAqJXkd5SUFKnW4yyXix6/ab9x7HtqZTcfdTMpOfNQOIiKTJDgBW\nr16NH/zgB3j88ceh0+kwe/ZsrF69Ws22Dbtkt53JTRiUyisAIHrbrQsmi45qw0cFxyq1WeEoLRS9\njitmjE6p000lGS+8br9icRUMBn1SmfzJ7sJgzQAiImmyA4CJEyfi5ZdfVrMtWSmZka5Uxx7uKAss\nRompaSdCItl1Tc3tuHrWGNFRrVDnD1wIVJYvqkQgGMInze3oOueNHNhz95Lp6Ow8ByC1rY5CyXiJ\nnieZ4CHVXRisGUBEJE12ALBr1y68/vrrcLvdQzqpN998U5WGZQu5nZXUmvMHB06j8UgbXG6f6EE6\nwGAGv1R2vdRUflmxBdWTy3DgWGdcoBLuRA+0tMPV60VJkRnVk+2DnahBr9hWx2SfR04mfzq7MISC\nt6tmjcGSKyfIviYionyV1BLAvffei1GjRqnZnqyVqLOSWnP2+ALw+AaTzsQ6f2CwjG8oFEKnO/4+\niaby51Q5sKKuSnD0vX5L85DHdPX6sK3pFAwGPR74+qWKbXVUestkuof/CAVv48aUwOl0J90WIqJ8\nIzsAGDt2LL761a+q2Zaclsx+dzHh7XJS09aJliRiAxV3nw8fHxY/fKe715vy4TzR1DipT6lEPjkz\nDUREWpMwADhx4gQAoLa2Fg0NDZg7dy6MxgsPGz9+vHqtyyHJVMsLKy2yoPucFyVFFkz7Uinq508C\noEO/ZwCHj7vgcnvjOni5SxLh6fh9h8WP73W5PfiP3+0XnHEI3y63k1Uj656JfKlj9UMiSiRhAPCP\n//iP0Ol0kXX///zP/4zcptPp8P7776vXuhwTPzq34JzHD48vGHffsmIrHv/mpXhr+zEcPu7CrkNn\n0NjsBBCCxxeE3WbGFdNHYcXiKSi0mOIen2hUK2e7oNlkwO5Dp0VvT6aTVaOzZiJf8lj9kIjkShgA\nbN26NeGTbNy4EfX19Yo0KJcJjc7f+vMx0Q7s7d1fYOehM5HfhfMEAKDT7cOHh86g0GpMev1c/gE8\n0tX3k+lk1eqsefhPcoTyMDbt+Ax9/b5hK11NRNlJdg6AlN///vcMAKJEj87FOrD6+ZPw9Mt7Ez6X\n1Pq52Ha7RAfwlBSZMX2ifUjwEWvejFGCnWzsa0b/rEZnLbbk4fUH0NHdl5HTGXOFGnkYRJS/FAkA\nxPauk3gH1ubqk3VKntD6eaLtdpLT8UUWPHP3ZTAY9NjX3CayPGHBHddPHTJlLPSaBRYjevv86D7n\nG9KGZIoDyRUOqgLBINZvac6a0xmzyXBUP8zkMdlEpCxFAgCdTqS4u4bFfjHGrtnL3TUgtH6eaLud\n1HT87Kpy9HsHsPmjE4KdPzC4GyH2y1zoNQHvkJ+j26BW1v1v3j+K9/ddOAkx/LqhUAjfWDxVldfM\nFZlMmsy2Y7KJKHmKBAB0gdwvRrm7BmLXz+VO8wolJBZaTdh/1Iltja2iB/JYzYbzuxEukJ9ToO5U\ns9cfwM6DwssWOw+ewW3XVCr2urk4ss1k0mQ2HpNNRMlhAKCwZL4YYztp8/kvaK8vAHuxFdWT7Vg4\nZyy8/kDky1vuNG+iA3jESgf7/AH09vmH7DxIlFMQrVPFg3acXf1DEiWjeXwBOLv6Mc5RlNZr5PrI\nNhPVD5lrQJQfFAkAiorS+9LNF8l+MQrlBwBAZ48HW/adxIGWdmxvOjWkEyqwGEXLCQtN81pMBows\nsuDAsQ5Z1yD0HMkUOSoZYVFv73miXBMFclFyfWSbieqHPGmRKD/IDgCcTifefvttdHd3D0n6e+CB\nB/Dzn/9clcblmlS/GGPzA7Y1tWJbY/w695HjXejz+EUL+4hN8yYzghdacuju9aK6snxIm8TMVnF/\nvqO0EFazXjB3wWo2wJFmp5NPI1s1qx+yQBNRfpAdANxzzz2YOnUqxo4dq2Z7cpoSX4xSndCJtl7B\n35cVS2+3k2qXXj84cLbHbNmLnQovtZkxvqIIfR6/6EzA+IoirKibkvAaU2UxGTBv5mhs3RcfiMyb\nOSrtzpkjW3lYoIkoP8gOAAoLC/Hcc8+p2Zacl+wXo1CiWTKjdWBwW99Td9bCVmhOqV03XDERV88c\nFZfsFjsV3un2odPtw8I5Y1BXOx5bPj6BA8c60dnjwcgiM+ZMKceKxVWqr5N//dop0Ot0aDziPF8q\n2YKaqQ5FCgNxZCsfCzQR5T7ZAcCsWbNw7NgxTJ48Wc325Dw5X4xSiWYjiywotZlF6/PH6j7nRb93\nQDIAAID6+ZPQ5xnA4S9c6Oq9cMbAt+tnorPz3JD7Ss1CHDjWiWWLpuCO66cNS6a83LMQUsGRrXxq\nfg5ElBmyA4AdO3bg1VdfRWlpKYxGI0KhEHQ6HbZv365i83JHdGeY6ItRLNEs/J72eYUz3YWYzyf5\niREKNq6cPgpfX1yFQosRBkP8iF3uVPhwnrKn1mtzZJscnrRIlLtkBwC/+MUv4n7X09Mj+Zgf//jH\n2LdvHwYGBnDPPfdg5syZePjhhxEIBOBwOLB27VqYzWZs2rQJr732GvR6PZYtW4alS5cmfyUZFN3Z\nGw060dG80Bej1Oj6gwOn4fULJbjp4R8IIiBct0eSULARLgF8+/XChXO0PBUeHtkumTcRJ9t6Ma6i\nKOHsChFRLpIdAIwdOxYtLS1wuVwAAJ/PhzVr1uCdd94RvP/u3btx9OhRNDQ0wOVy4ZZbbsGVV16J\nFStW4MYbb8QLL7yADRs2oL6+Hj/72c+wYcMGmEwm3HbbbVi8eDFKSkqUuUIFCY2mC62mIcl5ibaN\nSY2uhTp/ALCajfD4hJcEPL6AaHKaVLCx89AZ/O2LTsyeehH+Yf4kFFou/CloeSo81+sAEBHJJTsA\nWLNmDXbu3In29nZMmDABJ06cwN133y16/8suuwzV1dUAgOLiYvT392PPnj1YvXo1AGDhwoVYt24d\nJk2ahJkzZ8JmswEAampq0NjYiEWLFqVzXaoQGk2LZcSLbRtLZk99WPc5H8xGPXwDwtvfwiPy2DX5\nRAmFnW4ftn58Ah8eaMVXqscM6eS0OhWe63UAiIjkkh0AHDx4EO+88w7uuOMOvPHGGzh06BD+9Kc/\nid7fYDCgsHBwVLphwwZcffXV+OCDD2A2D06nlpWVwel0or29HXa7PfI4u90Op1O67GxpaSGMRpX2\nmjtsgr/3+AZkF9MBBov5DOj0GCfwfFfNGotNOz6T/VyhEDAgMv+v0+lQWlqI9ZuPYPeh03B29cNR\nUoArZozGN66fCkdpAdpc/ZLP7/EFseXjkygsMGNV/czI7x/4+qXw+Abg6vGitNgCqzk/Ckem8hkf\nONaBe24tyNn3QOya85XWrhfQ3jVr7XrVIPvbLNxx+/1+hEIhzJgxA88//3zCx23ZsgUbNmzAunXr\ncN1110V+L3aCoJyTBV2uPpmtTo7DYROtmNbm6oMzQUcaLQTgmf/6UHD6eMmVE9DX70NTczs63R5Z\nBezESvd6fQP49980DTnat83VHzkDvnpyWcLzBsJ27j+FG+eOj5u1MAJwd/dDuVpywyfVz7i9qx/H\n/rcjJxPepK45H2ntegHtXbPWrhdQJ+CRHQBMmjQJb775Jmpra3HXXXdh0qRJcLulP4AdO3bgl7/8\nJX71q1/BZrOhsLAQHo8HVqsVZ8+eRUVFBSoqKtDe3h55TFtbG2bPnp36Fakk2e15gPj0cfQWKmdX\nP1787SdJPW+0kiILDh93Cd7W1NyO1Svnnv9vZ8JlBznFbmKXGXLx0BwxWk5+JCLtkR0ArF69Gt3d\n3SguLsb//M//oKOjA/fcc4/o/d1uN3784x/j1VdfjST0zZs3D5s3b8bNN9+M9957D/Pnz8esWbPw\nxBNPoKenBwaDAY2NjXjsscfSvzKFWUwGjCgQDgCKCowwGw3odAt3sPsOO7Fk3sS4bHKLyYBxjiLU\nTK2QPUqPNe1Lpdh1SPiEPJfbg94+XyTYeGPzEXwocl9gcEthkUjGu1BlwBEFZvR5/HmTLKfl5Eci\n0p6EAcBf//pXXHLJJdi9e3fkd+Xl5SgvL8fnn3+OUaNGCT7u7bffhsvlwoMPPhj53Y9+9CM88cQT\naGhowJgxY1BfXw+TyYSHHnoIK1euhE6nw3333RdJCMwmXn8AfR6/4G0WkwHfvWUG/u9r+yA0U+/q\n9eLpdXtRO61CsIOMTrjrdHugg/iUf7TxFUVYsXgKjhx3JRy1WkwG3HXTNBRajfjgwGnBU/U8vgA2\n7vhMMNlNrDJgWL4ky2k1+ZGItCdhALBx40Zccsklggf+6HQ6XHnllYKPW758OZYvXx73+1deeSXu\ndzfccANuuOEGOe0dNtLFcbwwGw2S2f1dvT7RDjLR0b1i+jwDMOj1sket4de56YoJ+P4vd8MrsKtA\naPeC1HZCOY/PJaxwR0RakTAACE/Hv/HGG6o3JpslWh92lBSIdsTRpDrIcFW1FYurYDDoRUfqYeE1\n+2RHrT5/UHBLYfRzRucBJHM+Qb4cmsMKd0SU7xIGAHfccQd0Op3o7a+//rqiDcpWctaHwx3uvsNO\nuHpTP1UuPAqtn38xfr35CPb+7azgkkB4ij/ZUWuyyW7J1C4otVmSSpbLpyRCIqJckjAAuPfeewEM\nbufT6XS44oorEAwG8eGHH6KgoED1BmaTRCPt6DKyT6/bi67e+ITBZI4F7u3z4R9vnIYCi0FwSSB2\nil/uqDXZZDep+8cqtJpkdeT5XHGPQQ0R5YKEAUB4jf/ll1/Gr371q8jvr7vuOnz3u99Vr2VZSO5I\n21ZoRu004cz+RNnkQh3j7CnlWHTpWOw/2pFSYppQhxR+7IFjHWjv6k/4nLGJighBMOHxXL8fXn8g\nYceXjxX38jmoIaL8I3sb4JkzZ/D5559j0qRJAIDjx4/jxIkTqjUsm8kZaaeaTS7UMb6/rxV1teOw\nZtXlskaW4Q6/qNCEjTs+F+2QVtRV4Z5bC3DsfzsSPmd08PNZazfW/uYTwft19Xpl1RIQSyrM5STC\nfAxqiCh/yQ4AHnzwQdx5553wer3Q6/XQ6/VZuV8/W0jNFohNEcvpGKU61tgRqNmoH5LpL9QhWc3G\npJLdLCYDLh47EmVpFMyRe9xwLsnXoIaI8pfsAKCurg51dXXo6upCKBRCaWmpmu3KG9GzBYFgEG9s\nPoymox1w9/lRUmTGnCnlg1n/en3CjtHp6oPZZBAdrceOQIW2+QHiW/3krlunWzAnHyvu5WNQQ0T5\nTXYA0Nraiueffx4ulwtvvPEGfve73+Gyyy7DxIkTVWxe/vANDOB7P/sQvf0Dkd919fqwrekUWlp7\n8NSdtZIdo9lkwL9tOCC6tpzMXv3oDikQCGL9luak163TKZiTjxX38jGoIaL8Jjsz6cknn8TNN98c\nOaxn4sSJePLJJ1VrWL5Z89q+IZ1/tBNtvVi/5WikYxTi8QXQ0eNFCBem8hu2tkRuT2avfnSHtO4P\nn2LLxycln1tIeIljzarL8cNvX4E1qy7Hiroq2cluyxdVoq52HMqKrdDrgLJiK+pqx+VsxT2pzy5X\ngxoiym+yZwD8fj+uvfZavPrqqwCAyy67TK025R13nw+n2s9J3ueT5nYsW1gZN7IuKbKgzzsgWBAo\neip/ZJEFJUUW0foD0aZOGDybwesPYPeh04L3kbtunWrBnHysuMcywkSUS5I63LynpydSFOjo0aPw\neuWNOLXuZFtvwtr+XecuZM9Hd4y+gSCefnmv4GOip/ItJgNmV5VjW2Or6GtYzXoAOuw6dAZHjrsw\nbUIp2kSOv83UunU+VdzLx6CGiPKX7ADgvvvuw7Jly+B0OrFkyRK4XC6sXbtWzbYNG6ULuYyrKIJe\nJ33Ajz1mnTjcMXr9AdlryyvqpqDlZDdOtPXG3bfQYkSf98ISREePFzsPnUGBxYB+b/zsAtetU5dP\nQQ0R5S/ZAcCkSZNwyy23wO/34/Dhw1iwYAH27dsnehhQLko1IS4RW6EZYx1Fgh1zWHVlmWiwMW1C\nKXYKHOMrdNjPU3fW4plXPkKrc+iSQ3TnHy0kEpRw3ZqIKL/JDgBWrVqF6dOn46KLLkJl5eCa5sCA\ncKeSq8IJcWFKFnJ5/Js1+L+vN6LVOXQ5QIfBinr7jzph0OtQP38Sevv8kSI+jUfahhy7CwBWswFX\nzRwluLY8EAjBI9LZCwnnFoRnKOw2C2qmOpJet2b5WyKi3CI7ACgpKcFzzz2nZluGlRIJcVLMRiNW\n3z0X7j4fTrb14sNPT2PnwbORcrqd7sHjgj84cBpeXwAWs0H0JECPL4AQIDgr0d3rlXVoT6xwUDJr\nSrnsYMfrD6Czx4Mt+07iQEt7wlkTBglERNlDdgCwePFibNq0CXPmzIHBcOHLe8yYMao0LNO6e71w\ndqmfEGcrNOPisSOx7u2/Cd4e7vSljgEGgA8PnsHSayrjOtICizFhvoGUAy0d8C6UruUfXXEwNtgQ\nmjVhjXwiouwjOwA4cuQI/vCHP6CkpCTyO51Oh+3bt6vRrowbWWSBo6RAMCte6YS4ZPbsi/H4AnC6\n+jCuwjbk9/3egZQ7f0BesBNbcVBI9KwJa+QTEWUf2QHA/v378dFHH8FsNqvZnmFjMRlwxYzR2LTj\ns7jblE6Ik6oal5TzWzLjnttmjssbkCtRsCO34mA4kBhZZGGNfCKiLCR7/nXGjBl5v+//7iXTM1ad\nbuqE9M5SsJj1cJQUxP/eZMBskYp0csyeIr4bAZA/exEOJOTUyCciosyTPQNw9uxZLFq0CJMnTx6S\nA/Dmm2+q0rDhYDAoX8glOvEtEAzhv//UjMPHXejs8cJqPn86YIKkPyGXVlUIti0QDKL5RFfK7U20\neiB39iI8a8Ia+URE2Ul2APCd73xHzXZkFSUKucQmvlnMBvgHAghEHdAX7vCvmjEKX19cheffbJSs\nFRDNZNIjEAzGJdGt33IUJ9ukyw5L+aS5HdfMHgtHSYFggCF1kA8AlEUl+CW6P2sNEBENH9kBwNy5\nc9VsR96JTXyTGt0fPt4Fnz8AZ1ef7Of/c9MpmAz6IbMVwGAHLqVkhBkFFiNOdwq/Vqfbi6df3iua\nqR8IBhEKhWCNmrEw6Af/5xtA5LCoaGI18uvnX4w2V5/q2wKjZ2GIiGhQUmcBkDzJHM0LDK6F//q9\nI/D4gonvHOWDA6eHbK2bOqEUXRJr6rZCE1avnAuzyYDH/2uXaKJg9KmAwNBM/YatLXh/39DzBgJB\nRGY2wvUMoh8XWyM/XOTo6Zf3xG0LHAiEFFt+Edp+eNWssVhy5QRuPyQizWMAoIJkt/mVFFnw+Wl3\n0q/j8QUio/COHi8+PHRmyMg8Vu1UB2yFZnj9AXz5S3bB8sKxojP1kwlshDL8w0sr67c0C24LPHK8\nC30ev2K1AoS2H27a8Rn6+n3cfkhEmsdhUBq8/gDaXH3w+od2uOHEN7mmfakUXW7xgMFokP8xhUSK\nAIyvKMLyayuxfksznnhpN3YeOgOrWY8CixHxmwkviM7UTyaw6ezxwOmKX2aQCiJOtPWio8c7ZAai\nYWuLrNdL5nWamtvjPjMiIq3hDEAKElW2S5QoF2Y1G/CV6tGonz8Jjc1O0ZH7QED+0oB3IAizUQ+9\nXgevP4CSERbMrirHiropAnkJQQBBXHZJBQ4cbYfXH/860Zn6ydQvCAH4tw0H4kbxyc6OpForQM72\nQ57YR0RaxgBAhtga9nIq2y1fVIlAMIRPmtvRdc4b6cC8vgBKbRZM+1IpViyegkKL6fxoVH75PrvN\njD5vQDRg8A0MduTzZozCHddPTTh9f7ClQ7DzB4Zm6ltMBtGTCYUIvS9Fhaaktjym2llz+yERkTQG\nABKERvrVk8uw/1iH4P3Do1WjQYeGrS040NIOV68XJUVmzJlSjluvqURnjwcIheAoLYTFZEAgGMQb\nm5NLALxkkh1fnOlNuGXwyPEL9QCkRsRinbHVbED9/IuH/O7ri6uwr7ktqfZGj+I37vg8qXoHqXbW\nUrMw1ZPtPJRII3gAFZE4BgAShEb625pOid4/PFrdsu/kkMd19fqwrekUWlp74pLcgqEQPpQ5og7b\n+9e2yChfSvToOZXywz5/AL19PhRaLvyZFFqM+Er1mITLG0LtkCoLbNADQisd6dQKiN1+WFJkQYnN\nggPHOrC96RQPJcpjPICKKDEGACKS3coHDI5WCyxGySS3sPD0uNWc/JeRnM4/3J7w6FlqRCy2c0Bs\n9C20r7+6sgz7jzoFtxbKKQscCg0uWRw53jWkVkA6ZZhjtx9u/ugEtjVe2MLIQ4nyFw+gIkqMAYCI\nVE7sm1NVjn7vQFKPS3bvfzKqK8uGTH8KddxXzRqDc33euL39QPzoO3o69dYFk3F19WhAp4tUDTTo\ndZIV/xKty99x/VQAUHzKNvzaB1qEiyTxUKL84vEN8AAqIhkYAIhIdsr8qhmjIoVsFDnpT4JBr0NA\n4sxfq1kPR0kh9h91Yntj65Dpz9izDsaNKcGZs93Q6XRRgYEF0yaURtb/o6dTO3q852ctdPD6AkOe\nW6ziX7JlgdXIzueuAO1w9fCzJpKDAYAIo0GHQqtJVkdeWmTG7ddPhUGvh0EPWVsAw6QK9wixmPUI\nBUOC6+VhoZDwckOfZyCyKyD6CzA8VV4/fxLW/+koDn/RiQ8PncHh4y7MqXIgFAoNmSGInrWInVpN\ndJhSoiBBLdwVoB2lxfysieRgACCiYWuL7IN5vjzRPqSjE+rkCq1GwecrL7GianwJ9h/tiNx3yoSR\n2H3orOBr+XzBhBsGxbb0fXjoDI4cd6G6shx1l46Dvdg65PaNOz4fkpB4IU8h8XRpU3M7lsybiH7v\nAEYWWURHWLHr8pnKzs6WQ4mYla4+q9mYFZ81UbZjACAgmQRAq9mAFYunDPmdUCdnNOjw7KsfxwUB\nJ9vOYdqEUqxZdfmQA2uOHu8SHMGYTXrRDl6Ojh4vtjW2YltjK8rO18a/rnYsOnu8aDzSJvgYOTMU\nHT0ePL1uL7p7fbIyrpU4cTFZyxdVorDAjJ37T2V09gFgVnqmDddME1EuYQAgIJkEwK9Uj0ahxSR4\nW3Qn5/UH0OfxC95v32EnlsybOKRDFBvB6KTq9iYpXBv/vT1fwOsLJFGKSFhXry/yvNmYcW3Q67Gq\nfiZunDs+46NwZqVn1nDNNBHlEg49BEjV8tfrBjvhsmIr6mrHDRlRCJ0NEP6ds6tfPDGp14un1+3F\n+i3NCAQHR/fLF1WirnYcyoqt0J9/vSsuuUiVXQOeBJ2/nCUAIdlacz8cmKXbIYidBSF0P55LMDyU\n+qyJ8hFnAARIrRcvmD0G18+dMGREITS9O2tKOXQAPjnajs4eL0ptZskSuF29Q4/RjR7BdPZ4sGXf\nSdFtbGqbN3MU9Od3CXT2eGA5HxD4/AGMHGGBS+QI4nzNuE52Op87EIgoGzEAECG1hhj7JS80vbs1\nZl+9UIEcIbH7lC0mA7Y1tQ4pYJMJOgD24qHXHD2dCgx2bAUWI5599aOEGdf5kvzm9Qfw681HhpyH\nkGg6nzsQ1JMvf1dEw/4ET1kAAB3uSURBVIEBgAi5a4jJVgwUK3kbFl021+nqgy8QTLoiYbrKii14\n4LbqyHkFYbGJe+H/lsq4Nhp0WL+lOeFoOdu/yMOj/sYjbaLBnFiRmWzZgZBPpGZhiEgeBgAJJMpW\nT7ZiYKKTfUttFryz5wvs+evZhOv9Oh1w+SUXYfenwlsGY40cYcJAIIg+j/Sa/5wqB8ZV2GQ9JyA9\nW5Io+S1XsuNjr0OI1HQ+s9KVJfV39cDXLx2uZhHlFAYAaUrlkB0phVYT/vzJaVn3tdusuP26KhQV\nmNB4xIlOt3QbvvylUuz+a/xWP6vZAJ8/kHKnJDZbkij57dYFk/HWn49lfXa83Fkeqel8ZqUrJ9Hf\nlcc3kOEWEeWm7Bli5ajw9G669Drg6tmj0dsnP5CYU1WOQosJK+qq8OCyWZL31emAI8ddgrcVWox4\n5u65WLPq8kgCYipiM64TJb85XX05kR0vd5ZHznQ+s9LTl+jvyqViGW6ifMIAQAGxW/bsNguumHER\nrpkzOvK70gSJXnO/XIGbLv8SXL3CtQLCxLYgOkoKUFJkFn1cKATR5+7q9cJs1CveKUltpyy1WQGd\nLmF2fDaQug5gMGci9vMg9ST6uyqV+KyI6AIuASjgQi39i/Hff2rG4eMu7Dl0FvZiC6ory3H1rNEI\nBkL42f87KJhAZjUbcPv102DQ62C3mUWTzOw2Mx5cNjty+l40i8mAOVPKsa3plOhjAeHdCGploidK\nfnOUFOREdrzUdcybMSpyvgJlRqK/K6vZCPcwtIso16gaADQ3N+Pee+/FnXfeidtvvx2nT5/Gww8/\njEAgAIfDgbVr18JsNmPTpk147bXXoNfrsWzZMixdulTNZqlm447P4raHbWtsxa5DZ+D1BSL752MN\nVhMc/ChqplaIJpvVTK3AOEeR6OuvWFyFltYewTMHaqZWAEDGM9ETbafM9uz48O6E+vmTAMjbFkrq\nY1IlUfpUCwD6+vrwgx/8AFdeeWXkdz/96U+xYsUK3HjjjXjhhRewYcMG1NfX42c/+xk2bNgAk8mE\n2267DYsXL0ZJSYlaTVOFVGJSuPhP+P/Fku4CwSBCoRAsZj28UTsAzEYd5s8ag/r5k9Dm6huSQBa7\nfe6pO2uxfstRfNLcjq5zXthtVlRPtmPhnLEosBjQ5xnA4S9c6Or1otRmxVWzxmDJlRNUe18SJb+p\n9UWe7rZCsd0Jq1fORW+fj0l8w4xJlUTpUy0AMJvNeOmll/DSSy9Ffrdnzx6sXr0aALBw4UKsW7cO\nkyZNwsyZM2GzDW47q6mpQWNjIxYtWqRW01SRzHbAEVYjHru9Jm6ffcPWliHH7l6gw5HjXXjqV3vg\ncg8etjN7SjlCAPafrzQYvX3ujuumYtnCyiEVBLc1nYJeBwRDg8sBV04fha8vrsKXxpXC6VR/wlRs\nO6XSX+RKbStk7f7cMByHShHlC9XmLo1GI6zWocfN9vf3w2weXIsuKyuD0+lEe3s77HZ75D52ux1O\nZ2YL3yghUaJYNJfbC7PJMKSjk5pB8A0EcdJ5Dp1uH0IY7Ize39eKrfta0dHjjfxuy8cnsf5PzQAG\nvxi37DuJbY2tkTX24PnN/51uH3YeOoONOz5L+XqVplR2fLjjjn1fGra2yH4O1u4nIi0YtiTAUEi4\nFI3Y76OVlhbCaFRnus/hkF8AJ9ZVs8Zik4xO1WI2YPRFxfD6gygttsBqNuJ0+7mE+/jl2P7JKZhM\nBhgMevz5E+nywQeOdcDjG0jrmrOJxzeAA8c6BG87cKwD99xaACDxZyz1WbjcHhjMJjjKR6TX2AzL\nl89YLq1dL6C9a9ba9aohowFAYWEhPB4PrFYrzp49i4qKClRUVKC9/cIhN21tbZg9e7bk87hcfaq0\nz+GwpTUdvuTKCejr90UOzTGZ9PD546v59XsDuOdHW+D1BSNT1PXzJ8FuS7+gUCgEvLf3hKz7trn6\n8cu3DuBriyYLTo9ne3neWG2uPjhd/YK3tXf149j/dmB61UUJP+OAPyD6WZTarAj4/BlZNlFKun/X\nuUZr1wto75q1dr2AOgFPRtOX582bh82bNwMA3nvvPcyfPx+zZs3CwYMH0dPTg3PnzqGxsRG1tbWZ\nbJZiDHo9li+qRHVlGUqKLIKdf5jHFxwyRb1xx+eKFBRK1vsfn4ibHg8Eg1i/pRlPvLQb3//P3Xji\npd1DjipWmtixunKP2w1LtD9c7rZCqeJOhVYjjAadrOchIspmqs0AHDp0CM8//zxaW1thNBqxefNm\n/Ou//iseffRRNDQ0YMyYMaivr4fJZMJDDz2ElStXQqfT4b777oskBA6HdEe9DVtbUjq5r6m5HatX\nzkW/Z2DIVsJMiD3EJlMJcGIJe7ddczE2bP8s6UQ+JQ/dWb6oEkeOd8VtqTzR1ouGrS1MBCSinKda\nADBjxgy88cYbcb9/5ZVX4n53ww034IYbblCrKbIEgkG8tPEgdu5vTTl7PNmTAaO53B709vlw+/VT\n8df/7ZCsCFhWbMXsKWUYCAbxl6bTkgf7yH3t8CE2cur3K7UcIBZoxHa8yQQgSm0rHAiE0OcR/gyU\nfh+IiIYDKwGep8SoN9mTAaOFp6gtJgMunXaR4Ch2nGMEvls/A/Zia+Swnb7+AXx0WF7QYTbq4BuI\nDxeip8elrqGjx4POHg9Gl6WfACcVaLQ64wsZAfI6XqW2FSaqNy926h8RUa5gCTNId0b7Djvh7hMu\nzRsrma2AsaKnqKPPFtCdP0dgYc1YPH3XZRhdNgJGgy6yRv/RYScMCT5FvQ5YOGcM5s8ak/C1E13D\nlo/lJRgm0t3rFU14DIpMaSRzPkC62wqVyicgIspWnAFAgtFerxdPr9uL2mkVaa1Bi7HbLKiZ6hgy\nRZ1oFBs7WxFIkJsXAnD93AkoG2mFTqcbMj0eWwnQYjKgurJcNI/hwLFOeP2BtIv1bP7oRKQwUSyx\n32ey41Uyn4CIKBsxAMCF0Z7YiLSr15fyGnTxCDO6eoVnEHQ64MFls0Tr+wtVOZOardABgvkA9vMd\np1BgMW5MSdx2mrpLx4kGAEpMfydKlBzrKBI8zyDTHS/rzRNRPmMAAPkj91TWoAssRjz76keCwYXd\nZoWjpEDwecR2I0jNVoglA8Z2nInKp9qLrShT6ZQ+qQBGrwMWzB6D5ddWnt8FMLwdL+vNE1E+YwBw\n3vJFlSgsMGNHUytcIuvMyYx+ozvZZKaSE9Wyl5qtsNssmDWlHAdaOtLqONWc/k4UwFw/dwLMRmNW\ndbysN09E+YgBwHkGvR6r6mfi2jlj8PS6vYLT9qmOfpcvqkQgGBpyQp9Yx5xoN4JU51wz1YEVdVXw\nLkxcyyA8w2AbKTwDodb0t3QAM/T9ZcdLRKQeBgAxbIVm1E6rUGz0Gx7RH2hph6vXi5IiM6on2wUT\nCuXuwU/UOUt1nLEzDI7SAlRPLotrj1rT31IBzLQJuXUENBFRLmMAIEDJ0W/siL6r14dtTadgMOjj\nEgqltsZ19lxYfkinc45tT5urXzLBUY1ReOz7azYZAISw89AZHD7uSun4XiIiSg4DAAHJdLCxyXrR\nPwNIqqreyCILrGY9PL74fX0Wsx4+f2DIFrxw5xyumZ8oEMhklT8p0e/vG5uP4MOo0sdqlR0mIqKh\nGABISGYqvdRmxogCM/o8/kjy3rQJpaIj+tjyu+EdA4Ob+eJ5fEE8te4jlEUlBQKQTBgEhgYo2Vjd\n7shxl+DvWW6XiEhdDABSFDuV3un2odN9IXGwo8eLnYfOiI7oS21WFBWasH5Lc6QDH1lkhscnffJd\n9AgZgGjC4PJFlXHBQfXkMtEEvOGobpeNAQkRkVYwAEhBcof+CI/o51SVY+OOz+PyA+RqPOIU3fff\n1NyOQCCIbU2nIr/r6PFiW9MpjK8oEgwAhqO6ndSOACUDkthlGSIiYgCQkmQO/fH5A5g3YxSOHO8a\nklBYP/9iPP3ynpTb0OkWf/1OtwdNR9sFbzvX78fCmrGRWgHlJRd2AWSa2uV2hWoqXDVrLJZcOYEJ\nhkSkeQwAUpCodHC0UpsVd1w/FT5/ACfbejGuogi2QjPaXH2SQURpkQVd57zQQfxwHDElIyyixYy6\ner24/rLxWLawEt29XkyeWAZ3d39yL6AgNcvtCtVU2LTjM/T1+5hgSESaxwAgBckc+jN7Shne+vOx\nuES9+vkXiwYRZcVWPHVnLfq9A9i89/iQqXw5ZleV40BLu+TUejjB0Wo2wi3wHJmiVr2BbNnxQESU\nrRgApCh25FpSZMGIAhP6PH643N7ISDYYCuF9kUQ9qelvW6EZtkIzViyugsGgl3W4EABcNWMUVtRN\ngUGvy6mT7JSuN8AEQyIiaQwAUiQ2co1NOHvipd2Cj29qbsfqlZdF/lts+ju5w4UsuP36qTDo9Zo/\nyS5TCYZERLmKAUCaYkeu0T9LrfO73B709vkTTn9HBxSJDheqmeqIPF7rJ9mpnWBIRJTrGACoSO4o\nVGj6W+pUwGRG91o+UEfofbpq1hgsuXLCMLeMiGj4MQBQUTqj0ESnAmp5dC+X0CzIuDElcDqHM+2R\niCg7cDO0ypYvqkRd7TiUFVuh1w1m+NfVjpNci0+Uwe71D1YLDI/u2flL4/tERBSPMwAqS2Utnhns\nRESkNs4AZEgyo9Bw7oAQZrATEZESGABkoXDugBBmsBMRkRK4BJCltL6Pn4iI1MUAIEtpfR8/ERGp\niwFAltPyPn4iIlIPcwCIiIg0iAEAERGRBjEAICIi0iAGAERERBrEAICIiEiDGAAQERFpEAMAIiIi\nDWIAQEREpEEMAIiIiDSIAQAREZEGMQAgIiLSIAYAREREGsQAgIiISIMYABAREWkQAwAiIiINYgBA\nRESkQQwAiIiINIgBABERkQYZh7sBYT/84Q+xf/9+6HQ6PPbYY6iurh7uJhEREeWtrAgA9u7diy++\n+AINDQ04duwYHnvsMTQ0NAx3s4iIslcodOG/g8HB/wndFv3fUrfJvV8St+mg/HMiFAL0Pug63ed/\nTv/5h7Qz1XYZDAheNArQ6ZArsiIA2LVrF+rq6gAAkydPRnd3N3p7e1FUVJTxthSufQ7Gw3+78Asl\n/mCV+EcQ+webcrti7mc2YKQvEH+jQl8Gou1M5vlTbpfA/Yx6lAwElXlv5X6uanyxSj5u6E3QA/Zg\nSLW2pPxeKvL3FvuUIUAHlIWSe06d2Oeq9vuVVLukOWTfMz+UD3cDBPQ+sRr9/98/D3czZMuKAKC9\nvR3Tp0+P/Gy32+F0OkUDgNLSQhiNBuUbMjCAEetfB1pblX/uLGZW+gljI+Don+XepsRziNxmyvDr\nZeT5pW4LAIbwz7rYx+ny61rP/6yXuC3p58yC68nYbdnarlRvy+RrGwwouuXvUeSwIVdkRQAQK5Qg\n6nW5+lR5XYfDBufuT6Bzu4feMOTDh8RtyvxBhZC5P2ZHRTGcTrfwfZN5zhzhcNiGXq8GaO2atXa9\ngPauOauvV6V2OVQILLIiAKioqEB7e3vk57a2NjgcwzShZbEgZLEMz2sPB6Nx8H9ERKQpWbEN8Kqr\nrsLmzZsBAJ9++ikqKiqGZf2fiIhIK7Ji6FdTU4Pp06fja1/7GnQ6HZ5++unhbhIREVFey4oAAAC+\n973vDXcTiIiINCMrlgCIiIgosxgAEBERaRADACIiIg1iAEBERKRBDACIiIg0iAEAERGRBjEAICIi\n0iAGAERERBqkCyU6eYeIiIjyDmcAiIiINIgBABERkQYxACAiItIgBgBEREQaxACAiIhIgxgAEBER\naZBxuBuQLX74wx9i//790Ol0eOyxx1BdXT3cTUrLj3/8Y+zbtw8DAwO45557sHXrVnz66acoKSkB\nAKxcuRLXXHMNNm3ahNdeew16vR7Lli3D0qVL4ff78eijj+LUqVMwGAx47rnnMH78+GG+InF79uzB\nAw88gClTpgAAqqqq8K1vfQsPP/wwAoEAHA4H1q5dC7PZnBfXCwC/+93vsGnTpsjPhw4dwowZM9DX\n14fCwkIAwCOPPIIZM2bgV7/6Fd59913odDrcf//9WLBgAdxuNx566CG43W4UFhbiJz/5SeRvI9s0\nNzfj3nvvxZ133onbb78dp0+fTvuzPXz4MJ555hkAwNSpU7F69erhvcgYQtf8/e9/HwMDAzAajVi7\ndi0cDgemT5+OmpqayOP+//buPSiquo/j+HtZ2AQvCCh4G0uNktEG8hZI4GiiiYrjpUbGlSktvISX\n1BAdRp3JQpSahHLMSzmJjc0wzqijWVNp4whsEjOEmhWhE+IMgopcUpddvs8fPewjsaCGT7Dwff13\nfuecPd/P+c05+9vf7uzZt28f9fX1Lpf573mTkpJafb9qz3mhaebly5dz8+ZNACorKwkJCWHRokVM\nnz6d4cOHA+Dj40N6enqz1292djbvv/8+RqORyMhI3njjjZaLECUWi0Xi4+NFRKSoqEhefvnlNq6o\ndXJycuS1114TEZEbN27IuHHjZO3atfLdd9812q62tlYmTZokVVVVcvv2bZk6darcvHlTDh06JJs2\nbRIRkdOnT8uKFSv+9QwPIzc3V5YtW9aoLSkpSY4fPy4iIu+9954cOHCgw+T9O4vFIps2bRKz2Sy/\n/PJLo3V//PGHzJw5U+7evSvXr1+XyZMni81mk4yMDNm9e7eIiBw8eFC2bt3aFqXfV21trZjNZklO\nTpb9+/eLyKPpW7PZLAUFBSIismrVKjl16lQbpHPOWebExEQ5duyYiIhkZmZKamqqiIiMGTOmyf6u\nltlZ3kdxv2qveUWcZ75XUlKSFBQUSElJicycObPJ+uau3ylTpsjVq1fFbrdLbGys/Pbbby3WoV8B\nADk5OUycOBGAIUOGcOvWLWpqatq4qn9u9OjRbN++HYAePXpw+/Zt7HZ7k+0KCgp45pln6N69O126\ndGHEiBHk5+eTk5NDVFQUAGPHjiU/P/9frf9RsFgsvPDCCwCMHz+enJycDpv3o48+YunSpU7XWSwW\nIiIiMJlM+Pr60r9/f4qKihplbjg/7ZHJZGL37t34+/s72lrbt1arldLSUscsX3vL7yzzxo0bmTx5\nMvDXp8DKyspm93e1zM7yOtPR+7hBcXEx1dXVLc5CO7t+S0pK8Pb2pm/fvri5uTFu3Lj7ZtYBAFBR\nUYGPj49j2dfXl/Ly8jasqHWMRqNjGjgrK4vIyEiMRiOZmZnExcXx5ptvcuPGDSoqKvD19XXs15D7\n3nY3NzcMBgNWq7VNsjyooqIiFi9eTGxsLGfOnOH27duYTCYA/Pz8muQC187b4KeffqJv37707t0b\ngPT0dObNm8eGDRu4c+fOA2X28/Pj2rVrbVL//bi7u9OlS5dGba3t24qKCnr06OHYtuE12gtnmb28\nvDAajdjtdj7//HOmT58OgNVqZfXq1cydO5dPP/0UwOUyO8sLtOp+1Z7zQvOZAT777DPMZrNjuaKi\nguXLlzN37lzH137Ort/y8nKn56fFOlobpCOSDvLvyN988w1ZWVl88sknnDt3jp49exIUFMSuXbv4\n8MMPefbZZxtt31zu9n4+nnjiCRISEpgyZQolJSXExcU1mvF42FztPe+9srKymDlzJgBxcXE8/fTT\nDBw4kI0bN3LgwIEm2zvL5kp5/+5R9K2r5Lfb7SQmJhIaGkpYWBgAiYmJxMTEYDAYMJvNjBo1qsl+\nrph5xowZj/R+1d7zNrBarfz444+O3y707NmTFStWEBMTQ3V1NS+99BKhoaGN9mlNNp0BAPz9/amo\nqHAsX7t2zfGJylWdPn2anTt3snv3brp3705YWBhBQUEATJgwgV9//dVpbn9/f/z9/R0jx7q6OkTE\n8YmrPQoICCA6OhqDwcDAgQPp1asXt27d4s6dOwCUlZU5cnWEvPeyWCyOG2NUVBQDBw4Emu/je89F\nQ+aGNlfh5eXVqr7t3bt3oyl0V8m/bt06Hn/8cRISEhxtsbGxdO3aFS8vL0JDQx197uqZW3u/crW8\nDc6ePdto6r9bt27Mnj0bDw8PfH19GT58OMXFxU6v3+au9ZboAAAIDw/nq6++AuD8+fP4+/vTrVu3\nNq7qn6uurmbr1q18/PHHjl/RLlu2jJKSEuCvN43AwECCg4MpLCykqqqK2tpa8vPzGTVqFOHh4Zw4\ncQKAkydP8txzz7VZlgdx5MgR9u7dC0B5eTnXr19n1qxZjj79+uuviYiI6DB5G5SVldG1a1dMJhMi\nwiuvvEJVVRXwvz4ODQ3l1KlTWK1WysrKuHbtGk8++WSjzA3nx1WMHTu2VX3r4eHB4MGDycvLa/Qa\n7dmRI0fw8PBg+fLljrbi4mJWr16NiGCz2cjPzycwMLBDZG7t/crV8jYoLCxk6NChjuXc3FxSUlIA\n+PPPP7l48SKDBg1yev0OGDCAmpoarly5gs1m4+TJk4SHh7d4PH0a4H+lpaWRl5eHwWBg48aNjTrB\n1XzxxRdkZGQwaNAgR9usWbPIzMzE09MTLy8vUlJS8PPz48SJE+zdu9cxhRgTE4Pdbic5OZnLly9j\nMpnYsmULffv2bcNELaupqWHNmjVUVVVRV1dHQkICQUFBrF27lrt379KvXz9SUlLw8PDoEHkbnDt3\njg8++IA9e/YAcPz4cfbs2YOnpycBAQG88847eHp6sn//fo4ePYrBYGDlypWEhYVRW1vLW2+9RWVl\nJT169GDbtm107969jRM1de7cOVJTUyktLcXd3Z2AgADS0tJISkpqVd8WFRWxYcMG6uvrCQ4OZt26\ndW0d1cFZ5uvXr/PYY485PpgMGTKETZs2sW3bNnJzc3Fzc2PChAksWbLE5TI7y2s2m9m1a1er7lft\nNS84z5yRkUFGRgYjR44kOjoaAJvNRnJyMpcuXcJutxMbG8vs2bObvX7Pnj1LWloaAJMmTWLhwoUt\n1qEDAKWUUqoT0q8AlFJKqU5IBwBKKaVUJ6QDAKWUUqoT0gGAUkop1QnpAEAppZTqhHQAoJRSSnVC\nOgBQysUdPny4xfXff/99iw+PAZg/fz7Z2dmPsiylVDunAwClXJjdbmfHjh0tbrNv3z5u3br1L1Wk\nlHIV+jAgpVzY+vXrKS0tZcGCBURHR3Pw4EE8PT3x8/Nj8+bNHDlyhLy8PNasWUNKSgqXLl1iz549\nmEwm7HY7W7duZcCAAfc9zpUrV1iyZAlPPfUUgYGBvP7667z77rucP38egNDQUFauXAnAjh07OHXq\nFO7u7gQGBpKcnExZWRmLFi0iPDycvLw8fHx8iImJ4fDhw5SWlrJ9+3aGDh1KWloaubm5mEwmAgIC\nSE1NdZnnMijlckQp5bJKSkokIiJCSktLJTIyUqqrq0VEZMuWLZKRkSEiIuPHj5fLly+LiEhWVpaU\nlpaKiMjOnTtly5YtIiJiNpvlzJkzLR4nKChIfv/9dxEROXr0qMTHx0t9fb3YbDaZM2eOWCwWyc/P\nlxkzZojVahURkWXLlsmhQ4cc+xcXFztqaqgvPT1dNm/eLJWVlRISEiI2m01ERI4dO+aoVSn16OkM\ngFIdwIULFxg2bJjjv+LHjBnDwYMHm2zXq1cv1q5di4hQXl7e5BGrLfH29mbw4MEAFBQUEBYWhsFg\nwGg0MmrUKAoLCzEajYwePRoPDw9HHYWFhYwePRofHx/H8ykCAgIYMWIEAH369OHq1at4e3sTERGB\n2WwmKiqK6Oho+vTp06rzopRqnv4GQKkOSEQwGAyN2urq6li5ciVvv/02mZmZzJ8//6Fes+FNHWjy\n2g3Ha64dwGg0Nlp377L895Ek6enpbN68GQCz2czPP//8UDUqpR6cDgCUcmFubm7YbDaGDx/O+fPn\nqampASA7O5vg4GDgrzdrm81GbW0tbm5u9O/fn7t37/Ltt99itVr/0XFDQkLIzs52PIr2hx9+IDg4\nmJCQECwWC3V1dQDk5OQ46rifkpIS9u3bx5AhQ1iwYAFRUVFcvHjxH9WnlLo//QpAKRfm7+9Pr169\nWLp0KfHx8bz66quYTCb69OnDqlWrAHj++edZvHgxqampTJs2jTlz5tCvXz8WLlxIYmIiX3755UMf\n98UXXyQ/P5/Y2Fjq6+uZOHEiI0eOBGDq1KnMmzcPNzc3hg0bxrRp07h69ep9XzMgIIALFy4wZ84c\nunbtire3NwkJCQ9dm1LqwejjgJVSSqlOSGcAlFLAX1Pw69evd7pu/fr1BAUF/csVKaX+n3QGQCml\nlOqE9EeASimlVCekAwCllFKqE9IBgFJKKdUJ6QBAKaWU6oR0AKCUUkp1Qv8BHxldmT7aXEcAAAAA\nSUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "t0lRt4USU81L",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This initial line looks way off.  See if you can look back at the summary stats and see the same information encoded there.\n",
+        "\n",
+        "Together, these initial sanity checks suggest we may be able to find a much better line."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AZWF67uv0HTG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Tweak the Model Hyperparameters\n",
+        "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
+        "\n",
+        "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
+        "\n",
+        "For each period, we'll compute and graph training loss.  This may help you judge when a model is converged, or if it needs more iterations.\n",
+        "\n",
+        "We'll also plot the feature weight and bias term values learned by the model over time.  This is another way to see how things converge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wgSMeD5UU81N",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
+        "  \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label]\n",
+        "\n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Output a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kg8A4ArBU81Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1:  Achieve an RMSE of 180 or Below\n",
+        "\n",
+        "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
+        "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 939
+        },
+        "outputId": "c5cf9f13-f084-46be-e45e-54afe9c82444"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1\n",
+        ")"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 236.32\n",
+            "  period 01 : 235.11\n",
+            "  period 02 : 233.90\n",
+            "  period 03 : 232.70\n",
+            "  period 04 : 231.50\n",
+            "  period 05 : 230.31\n",
+            "  period 06 : 229.13\n",
+            "  period 07 : 227.96\n",
+            "  period 08 : 226.79\n",
+            "  period 09 : 225.63\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean          13.2    207.3\n",
+              "std           10.9    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            7.3    119.4\n",
+              "50%           10.6    180.4\n",
+              "75%           15.8    265.0\n",
+              "max          189.7    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>13.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>10.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>15.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>189.7</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 225.63\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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YMWOwdu1aBAQEPPCaIc8VysrK8NJLL2kmQASAK1eu4MyZM/Dx8YG3tzfi4+ORm5uLO3fu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\n1+1MYWkVtHpxwRZPmpuZbPP+RoUqkTU2nn0YiIg6Yfbs2Vi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9hOXLlwMApk2bhjVr1iAx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+apn8O2WS7m97apWddlTUW7++9YR/F19Sx8Xe57PiIioq4klUpwz6REDO4XgVVbi/Hv/5zBifNq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\n8mbw8y5HfgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 939
+        },
+        "outputId": "eaf77793-acfa-422a-8b00-a9f9b24107db"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.42\n",
+            "  period 02 : 204.04\n",
+            "  period 03 : 195.33\n",
+            "  period 04 : 186.92\n",
+            "  period 05 : 180.27\n",
+            "  period 06 : 175.44\n",
+            "  period 07 : 171.07\n",
+            "  period 08 : 169.08\n",
+            "  period 09 : 167.30\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         116.9    207.3\n",
+              "std           96.4    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           64.6    119.4\n",
+              "50%           94.0    180.4\n",
+              "75%          139.3    265.0\n",
+              "max         1676.8    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>116.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>96.4</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>64.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>94.0</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>139.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1676.8</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 167.30\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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QAS9wrwAuIp50j9BptYwekMT1fRI96kohLk7H3/0M1ekiYfxYNFrPLlq1+7ah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DxFQKq12LlzJ0lJSSxevBiLxcLEiRO56aabmDZtGqNHj2b+/PkkJCQwYcIE3n//fRISEjAYDEya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CIAjJMRZ+vHQs+ys7eP2TcjbubWT7wRbmTU7hyokpJ11hLAjD3VkrSpwoOfvxxx/nv/7rv1ix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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 939
+        },
+        "outputId": "d2d70a48-62ab-4204-bd86-3d521221c7fc"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.62\n",
+            "  period 02 : 205.05\n",
+            "  period 03 : 196.59\n",
+            "  period 04 : 189.39\n",
+            "  period 05 : 183.81\n",
+            "  period 06 : 180.67\n",
+            "  period 07 : 178.36\n",
+            "  period 08 : 176.73\n",
+            "  period 09 : 176.03\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         119.5    207.3\n",
+              "std           96.0    116.0\n",
+              "min            0.3     15.0\n",
+              "25%           66.0    119.4\n",
+              "50%           97.6    180.4\n",
+              "75%          143.9    265.0\n",
+              "max         2983.0    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>119.5</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>96.0</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>66.0</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>97.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>143.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2983.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 176.03\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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LpzMVurbRM3GICb2+/gUSaRlWXpt3jMRjRbRo5svDd0UT1diz4ZSoHuMHtOb3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GS4gacWxhEb3bp2rfMKHHzw2VX2HMxHrYKp48O4Oz4SL5Pn/TtaM9m5djZty2T77lysNhlvLw0z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\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ci1ISxxrZ7v0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SjdQQCduZ7BV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb
new file mode 100644
index 0000000..bd255eb
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,1838 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "improving_neural_net_performance.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "jFfc3saSxg6t",
+        "FSPZIiYgyh93",
+        "GhFtWjQRzD2l",
+        "P8BLQ7T71JWd"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/improving_neural_net_performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "cellView": "both",
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "eV16J6oUY-HN"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Improving Neural Net Performance"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "0Rwl1iXIKxkm"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n",
+        "\n",
+        "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "lBPTONWzKxkn"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, we'll load the data."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "VtYVuONUKxko",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "B8qC-jTIKxkr",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ah6LjMIJ2spZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1153
+        },
+        "outputId": "5ffc55c7-afb7-498b-f8af-905a55bad241"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2637.0           538.5   \n",
+              "std         2.1        2.0                12.6       2172.4           422.0   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1453.8           295.0   \n",
+              "50%        34.3     -118.5                29.0       2122.5           432.0   \n",
+              "75%        37.7     -118.0                37.0       3153.0           650.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1428.1       501.0            3.9               2.0  \n",
+              "std        1130.9       387.3            1.9               1.1  \n",
+              "min           6.0         1.0            0.5               0.0  \n",
+              "25%         785.0       280.0            2.6               1.5  \n",
+              "50%        1166.0       407.0            3.5               1.9  \n",
+              "75%        1723.0       606.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2637.0</td>\n",
+              "      <td>538.5</td>\n",
+              "      <td>1428.1</td>\n",
+              "      <td>501.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2172.4</td>\n",
+              "      <td>422.0</td>\n",
+              "      <td>1130.9</td>\n",
+              "      <td>387.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1453.8</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>785.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2122.5</td>\n",
+              "      <td>432.0</td>\n",
+              "      <td>1166.0</td>\n",
+              "      <td>407.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3153.0</td>\n",
+              "      <td>650.0</td>\n",
+              "      <td>1723.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.6       2659.7           541.5   \n",
+              "std         2.1        2.0                12.5       2198.1           420.3   \n",
+              "min        32.5     -124.3                 2.0         15.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1474.8           301.0   \n",
+              "50%        34.2     -118.5                29.0       2140.5           438.0   \n",
+              "75%        37.7     -118.0                37.0       3146.0           647.0   \n",
+              "max        42.0     -114.5                52.0      32054.0          5290.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1433.2       501.8            3.9               2.0  \n",
+              "std        1187.5       377.7            1.9               1.4  \n",
+              "min           3.0         2.0            0.5               0.3  \n",
+              "25%         801.8       287.0            2.6               1.5  \n",
+              "50%        1169.0       412.0            3.6               1.9  \n",
+              "75%        1714.2       602.0            4.8               2.3  \n",
+              "max       35682.0      5050.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2659.7</td>\n",
+              "      <td>541.5</td>\n",
+              "      <td>1433.2</td>\n",
+              "      <td>501.8</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2198.1</td>\n",
+              "      <td>420.3</td>\n",
+              "      <td>1187.5</td>\n",
+              "      <td>377.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1474.8</td>\n",
+              "      <td>301.0</td>\n",
+              "      <td>801.8</td>\n",
+              "      <td>287.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2140.5</td>\n",
+              "      <td>438.0</td>\n",
+              "      <td>1169.0</td>\n",
+              "      <td>412.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3146.0</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1714.2</td>\n",
+              "      <td>602.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32054.0</td>\n",
+              "      <td>5290.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5050.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                206.8\n",
+              "std                 116.2\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 179.9\n",
+              "75%                 264.1\n",
+              "max                 500.0"
+            ],
+            "text/html": [
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                208.6\n",
+              "std                 115.5\n",
+              "min                  17.5\n",
+              "25%                 121.5\n",
+              "50%                 181.4\n",
+              "75%                 267.9\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>208.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>17.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>121.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>181.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>267.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "NqIbXxx222ea"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Train the Neural Network\n",
+        "\n",
+        "Next, we'll train the neural network."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "6k3xYlSg27VB",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "De9jwyy4wTUT",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural network model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "W-51R3yIKxk4",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    my_optimizer,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A tuple `(estimator, training_losses, validation_losses)`:\n",
+        "      estimator: the trained `DNNRegressor` object.\n",
+        "      training_losses: a `list` containing the training loss values taken during training.\n",
+        "      validation_losses: a `list` containing the validation loss values taken during training.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor, training_rmse, validation_rmse"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "KueReMZ9Kxk7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "259a2534-19aa-4a31-d4ed-6e27889866f8"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 152.97\n",
+            "  period 01 : 151.09\n",
+            "  period 02 : 126.02\n",
+            "  period 03 : 115.39\n",
+            "  period 04 : 105.79\n",
+            "  period 05 : 102.53\n",
+            "  period 06 : 102.28\n",
+            "  period 07 : 104.41\n",
+            "  period 08 : 102.15\n",
+            "  period 09 : 101.02\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   101.02\n",
+            "Final RMSE (on validation data): 100.20\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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XBZh65pLOkZRn6tECIiJSuynA1DNhgT60CWyJUeLFutTNlNrLXF2SiIjIOVOA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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "flxmFt0KKxk9"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Linear Scaling\n",
+        "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Dws5rIQjKxk-",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def linear_scale(series):\n",
+        "  min_val = series.min()\n",
+        "  max_val = series.max()\n",
+        "  scale = (max_val - min_val) / 2.0\n",
+        "  return series.apply(lambda x:((x - min_val) / scale) - 1.0)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MVmuHI76N2Sz"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Normalize the Features Using Linear Scaling\n",
+        "\n",
+        "**Normalize the inputs to the scale -1, 1.**\n",
+        "\n",
+        "**Spend about 5 minutes training and evaluating on the newly normalized data.  How well can you do?**\n",
+        "\n",
+        "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n",
+        "\n",
+        "Sanity check your normalized data.  (What would happen if you forgot to normalize one feature?)\n"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "yD948ZgAM6Cx",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "556403c8-5d0b-43b8-8337-94c2fe1f0094"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize_linear_scale(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+        "  processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+        "  processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+        "  processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+        "    steps=2000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 164.04\n",
+            "  period 01 : 111.14\n",
+            "  period 02 : 98.15\n",
+            "  period 03 : 82.18\n",
+            "  period 04 : 76.62\n",
+            "  period 05 : 74.27\n",
+            "  period 06 : 73.04\n",
+            "  period 07 : 71.72\n",
+            "  period 08 : 70.90\n",
+            "  period 09 : 70.28\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   70.28\n",
+            "Final RMSE (on validation data): 71.25\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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9SLmr3Oo4IiIiDYIKjIeFNwkgqKw5pq2Cbel7rI4jIiLSIHi0wOzZs4fhw4fz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\nQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "jFfc3saSxg6t"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Ax_IIQVRx4gr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n",
+        "\n",
+        "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "D-bJBXrJx-U_",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize_linear_scale(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+        "  processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+        "  processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+        "  processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+        "    steps=2000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MrwtdStNJ6ZQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Optimizer\n",
+        "\n",
+        "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+        "\n",
+        "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate.  This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training.  You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`.  Note that you may need to use a larger learning rate with Adagrad.\n",
+        "\n",
+        "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad.  To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "cX_kIWqwlPqE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "f8774d28-a2a4-4447-d61f-ad2a9930202c"
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 80.51\n",
+            "  period 01 : 75.12\n",
+            "  period 02 : 72.69\n",
+            "  period 03 : 71.37\n",
+            "  period 04 : 71.61\n",
+            "  period 05 : 72.24\n",
+            "  period 06 : 69.43\n",
+            "  period 07 : 69.25\n",
+            "  period 08 : 69.49\n",
+            "  period 09 : 69.26\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.26\n",
+            "Final RMSE (on validation data): 70.52\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ScspIyy41PN/fqS9edp4k5ByhtKashSMIIYQQojWkUGmnS5NqYy6bVKsoCmN9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\nhr1lx0cFTcGc89LTSW5ap6VCRS79iA7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77Tjcbd5LfwPqc0RRRHltE2yOlpC2MzXtJgyx\nZONoXSG2ln0X0raIrmXS3O6T6DIZtEr824yhuG5oP6zcXIBNu05jf2ENHrorB4PT+t7Vfakrn0/E\n8bMNOFBcgwPFtai2u6DXKLDs4QkhO3NMJsjwwNC5eDbvr1hfsgmDYgci3ZQakraIrmXypUuXLo10\nJy6mudkdsu+t16tD+v3pyl1NbRLMWtw0KhltHh8Ol9Thu8OVaGhsRVZKrCQvchhtoul9427z4tCJ\nOmzafRorNxfg6/1nUXLWAY9PxKDkGFTYmnGywoFJw5NCtiePWq5Gf0MSdlfuQ6G9BNcljYNS1vt/\nH0ZTXfoa1qZn9Prg1xcDOKJC1yC1So55U7MwfkgC3tpYgG0Hy/FDSR0W3J6N3Kz4SHePQsjZ7Mah\nkjrsL6pB/kkb3G3+tSExehWmjE5GblY8hqTHQiGX4eWPj2BvYQ0+3XESM28cGLI+DbFk4/b0W7Dl\n1Fa8X/ghHhp6n2Q3KySSopAFlbVr12LDhg2B+0eOHMHq1avxxz/+ETKZDCaTCc8//zy02vBebZb6\njszkGCxZNB4bd57CJztO4sV1hzFhSALmT8uGSa+KdPeol1TXu3CwyD+lU3SmHh07QyVZdRidFYcx\nWfEYmGy6YK+dB+/KQWmFA+u/L0VOuhnZqbEh6+OPBt6OYnsJ9lYdRI45C9cnjw9ZW0TXmrBs+JaX\nl4dNmzahuLgY//mf/4mRI0di+fLlSElJwf3333/R47jhW98UitqcrWnEW5sKUFLugF6jwLypWZg0\nvB//sr1MUnjfiKKIU1VOHCiqxYHiGpypaQIACAAy+8cgNysOo7PikGTVX/J7FZXVY/l7+2E2qrHs\n4QnQa0J3/aQ6lw1/2fMCvD4vfjv+V+inT+y17y2FulBwrE3PdLfhW1imflasWIH//u//hlarhcFg\nAABYLBbU19eHo3ki9I834HcLxuKr/Wfw4Tcn8Ppnx7D7aBUeuHMw4mI4qid1Hq8PhWX1ONA+cmJ3\ntgIAFHIZRmZaMSY7HqMGxSHmMkfKslNjMeuGgfj4u1K8takAj/5keMjCq1Vrwfycn+L1I+/gjfz3\n8Juxj0XkwpJE0SbkQeXQoUNISkpCfPy5tQHNzc1Yv349/va3v4W6eaIAmUzAbeNSkTsoDm9/Xogj\npTY8/Voe5kzJwNQxKZDJOLoiJa5WDw6fqMOB4locKqmDq9UDANBrFLh+WD/kZsVheIYFGtXVfYz9\naNIAHD1lx77CGnz7QzmmjO7fG90PakzCSBT2vw7fnd2Fj45/irmDZ4esLaJrRcinfp555hnMmDED\nEydOBOAPKb/85S8xa9YszJkzp9tjPR4vFIrwXNSL+hZRFLF13xm8tv4wnM1tGJxuxq/uHY20fqZI\nd61Pq2twIS+/ErvyK3GouDaw03CCWYuJw5Nw3fB+GDrQCoW8d8/gqrG78Kvnt8Lt8eGvj98U0teB\n2+PG775cjrKGcjxxw79jYkpuyNoiuhaEPKjccccd+OSTT6BSqeDxePCzn/0MM2bMwD333HPJY7lG\npW8KZ20amtxY/WUR8o5VQy4T8ONJAzD9+vRe/0V4rejt2oiiiPK6ZhwsrsH+olqUVjgC/5aWYEBu\ndjxys+KQmmAI+XqifYXVWPHREaTEG/D0g2OhDOEfSeWNlXhu74tQyBT43fjHYdVe3V4//DyTLtam\nZyK2RqWqqgp6vR4qlX/e+J///CcmTJjQo5BCFA4xehV+MWs4Jg6twTtbivDxd6XYU1iNh+7KQWZy\nTKS7d03y+USUlDcEFsNW2V0AAJkgYEi6GaOz4pCbFRf2tUNjByfg5tz+2HbgLP61tQT335YdsraS\nDf1wT/ZMvFewDm8dfQ+P5/4CchlHj4mCCWlQqampgcViCdx/9913kZKSgp07dwIAJk6ciMceeyyU\nXSDqkdyseAxONeODbcex7WA5nn17H24bn4rZkzOgVvEXyNVyt3lx9KQdB4prcPB4LZzNbQAAtVKO\nsYPjMSYrHiMyrTBoI7u4dO6tg1BUVo+v9p3BsAEWjM6KC1lbk5ImoNB2HPuqf8DG0i/w48w7Q9YW\nUTQLy+nJV4pTP31TpGtTeNqOtzYVoMruQlyMBg/elYNhAyyXPrAPuJzaNLra8MPxWhworsWR0rrA\n5msmvQqjB/lHTYYOMId0iuVKnKluxB9X7oVGJceyhyfAbLz4jplXy+Vx4S95f4OtxY7HRv8MOZas\nK/o+kX7P0MWxNj3T3dQPgwpJjhRq427zYv33pfh8dxl8oogbRyRh7tRBId1nIxpcqjY19S4cKK7F\nweIaFJU1wNf+8ZJo0WFMVhxys+OREWTzNan5ev8ZvLOlCDlpsfjNvNyQnhF20nEaz+/7HxiUeiye\n8GsYVYbL/h5SeM9QcKxNz0R8HxWiaKNSynHPzYMwIScRb246hu8OV+DQiTosuC0bYwfHc6O4dqIo\n4nRVIw60L4Y9U9MY+LfMZFNgMWxPNl+Tklty+yO/1IYDxbXYtPsUZlw/IGRtDTClYWbGnfi4ZCPe\nProGvxy1CDKBi7mJOjCoEHUjvZ8Rf3hgHLbsKcP670rxPx8fQW5WHBbcPjikUwJS5vH6UFRW718M\ne7wGNkfH5msCRmZaMTorDqMHxSHWEL3/P4IgYNH0ITj5Rh4++rYUOWlmZPYP3eLqqWk3odB+HEdt\nhfi6bDumpU0JWVtE0YZTPyQ5Uq1Npa0Zb20qQFFZPbRqBebeOgiTR4buyrtS4POJqG9shc3Zihq7\nC8XlDuTlV6K5ffM1nVqBkYOsGJMVj2EDLdCqr62/fQpO2fF/Vx+ANUaDpYsmQKcJ3c/ndDfi2by/\norGtCb8Z+7+Qbkrt8bFSfc8Qa9NTXKMSBF880iXl2vhEEd8eLMfabcfhavUiJy0WD96Vg0SzLtJd\nu2yiKMLpaoPd0QqbowU2ZyvqHC2Br+2OFtid7sA6kw4Wkxq5Wf4pnezU2Gt+z5kPvz2BT3ecxIQh\nCfj5zGEhDaYFtmK8dPA1WDVmPDXhP6BV9OwUbSm/Z/o61qZnGFSC4ItHuqKhNjZHC97ZUoSDx2uh\nVMgwe3IGbhufArlMOr+0Xa2eQOiwOVpg6xRIOm7bPL6gxwoCEGtQw2rSwGJSw2LUwGxSY+KIZBhV\nsmt6FOl8Xp8P//XufpScdeDh6UNw48ikkLa3oWQzPj/1NcYmjMKiYfN79H8dDe+Zvoq16RkGlSD4\n4pGuaKmNKIrYU1CNd78ogrO5Den9jFh0Vw7SEi/+hustbR5ve+BovTCMOP23HdfGCcaoU8JibA8h\nncJIRzCJMaiChq5oqU1vq613YcmbefD5gCWLxqOfJXQjaF6fFy8ceAUnGk7h/pyfYlLyhEse01fr\nEg1Ym55hUAmCLx7pirbaNLra8P5XxdhxpBJymYA7J6Zh5g0Drnh/EK/Ph4ZGd5fQYXO0+Kdl2qdk\nHO0bpgWjVcv94aMjiBg7wsi5+1fat2irTW/KO1aFV9bnIy3RgN8vHAelInSjZ3UuO/6y5wV4fB78\ndvyvkKRP7Pb5fbkuUsfa9AyDShB88UhXtNbmyIk6rNxcgDpHK/pZdHjorhxkp8Z2eU7HupCgUzHt\nwaQ+yLqQDgq5DBZT+5SMUQ1ze/jouG8xaUK6oDVaa9Nb3th4DN8dqsDt41Mxb+qVbc7WUwerD+Of\nR1YhWd8PT47731DJL76HT1+vi5SxNj3DoBIEXzzSFc21aXF78OE3J/DVvjMQAVw3NBFymXBuNKSb\ndSEyQUCsUdU+GqIO3FrbR0PMJjWMWmVE14dEc216Q6vbi2Vv7UGlrRm/vncURmRYQ9re+4UfYfvZ\nnZjc/3rMGzz7os/r63WRMtamZ7jhG1GYaFQKzL8tGxOGJuLNjcew62hV4N9MOiX6x+m7BhHTuUBy\nsXUhJB1qlRw/nzkM/2fVXrz+6VEse3gCYkK4X8ycQT9CSX0ptp/dicHmQchNGBGytoikiiMqJDnX\nSm08Xh9OVzXCoFXAfBXrQqTkWqnN1fpiTxlWf1WMYQMt+PW9o0J6SYDKpios3/N3yGVy/G7847Bq\nL7zuFOsiXaxNz3Q3osI/34hCRCGXISPZhASz7poIKXTOtHEpGJlpRX6pDVvyykLaVj99Iu7J/glc\nnha8mb8aXp83pO0RSQ2DChHRZRIEAQ9PH4IYvQrrvilBaYUjpO1dnzQO4xJHo9RxCp+WbglpW0RS\nw6BCRHQFTHoVfvbjofD5RLy6Ib/bfWuuliAImDd4DuI0FnxxahsKbMUha4tIahhUiIiu0LABFtw5\nMQ3Vdhfe/aIopG1pFRo8PPx+CIKAlUffh8PNdQ/UNzCoEBFdhdk3ZWBgkhE7jlRiZ35lSNtKN6Vi\nVuZdcLidePvoGvjE4Ke6E11LGFSIiK6CQi7Dz2cOg0Ylx6rPC1Ftbw5pe7emTsZQ62AcsxXhq9Pf\nhrQtIilgUCEiukoJZh0W3jEYLW4vXt2QD483dCMdMkGGB4bMRYzKiA0nNqO04XTI2qKe8/q8qGmu\nQ35dIbaVfY9/Fa3HioOv4/nv/4FtZd/jbGMFR8CuEDd8IyLqBdcP64cjJ2zYmV+Jj7afwD03DwpZ\nW0aVAQ8OvQ8vHvwn3sx/F8PTng5ZW3SOT/TB3lKPalctappru9zWuezwikFOHbcBu3EAAGBQ6jEo\nNgNZ5gxkx2YiSZ/Yp65EfqUYVIiIesmC27NRUt6AzbtOY+gAC4YNuHBztt4y2DIId6Tfgs2nvsb/\n5L2Nm/rdAJPKBJPaCKWMH+1Xyif60NDqQPV5QaSmuRa1rjp4goQRvVKHNGN/xOvikKCN63KrMQrY\nVXIIxfUnUGQvwcGawzhYcxiAP7hkmTORHZuBLHMm+ukSGFyC4M60JDmsjXSxNpdWWuHAs6v2waBV\nYtm/TYBJpwpZW16fFy8ceBUnGk52eVyn0MKkNiFGZYRJZUKM2tj+tfHc42oTNHJ1n/zFKIoiHG4n\nqptrUeOqPe+2Dm2+C69OrlVo28OH9VwYaQ8kOqXuom11fs+Iooi6FhuK7CUosp9AcX0J6lsbAs81\nKg3IMmcgKzYT2eZMJOri+0x9eFHCIPiBK12sjXSxNj2zafcprN1agpGZVvzHT0eG9JdNc1sz8hvz\nUVZbhQa3Aw53IxytDjS4nXB5XN0eq5IpYVKbYFIZA+Gly63KiBi1CXqlDjIhupY0iqKIxramoCMj\nNa5atHrdFxyjlqsuGBFJ0MUhXhsHg1J/RXXs7j0jiiJqXHUori9Bsd0/4tLgPrd5oFFlQHZsZmDU\nJeEaDi4MKkHwA1e6WBvpYm16xieK+Ouag8g/acd9U7Nw2/jUkLZ3sbq4vW1wuJ1wuB1wtDrR4HYG\nQkyD2wFn+2NOdyNEXPxXgUyQ+UdjVEbEqNtHZtpHas6N2JhgVBmgCPO0U1Nbc5CRkRrUuOrg8rRc\n8HylTIl4rdU/GqKLR7z2XBgxqQy9HgQu5z3jDy61KLKXBKaKOu+XE6MyIsuciaz2qaIEbdw1E1wY\nVILgB650sTbSxdr0XENjK555Iw+uVg/+8MA4pCVe/IP4al1tXbw+LxrbmvwjMq1OONxONLT6A07n\ncONwO+Hxdb8Dr16pQ4zKFBiN8U83GbtMQ5lUJmgUPb/qtMvj8oeQ9lGR6uY61LSPjjR5LjwdXCFT\nIE5r7TJV0xFGYtSmsI4OXU1tRFFEdXMNiupPoNhegqL6EjjdjYF/j1GZAgtzs8yZiNdaoza4MKgE\nwQ9c6WJtpIu1uTyHSurwwtof0M+iw5KHxkOtCs3FKcNVF1EU4fK4/CMyrY720ZpzX5+7daLFe+Fo\nRmcqueq8NTT+hcAGpQEOt6PLKEljW9MFx8sEGeK0li5TNR0jJGZNjGSmqnqzNqIooqq5BsX1Jf5R\nF/sJONvOBZdYdUz7+hb/Opc4rSVqgguDShD8wJUu1ka6WJvL9/5XxdiypwyTRyZh0fQhIWlDinVx\ne92B0NIQmHpqDzedHmtsa7rotJMAAdbzwkjHrUUTC7lM+lclD2Vt/MGl2r84t33UpXOoM6tjuyzO\ntWrMkg0u3QUVnsNGRBRCd0/JRMFpO7YfqsCwgRZMGJIY6S6FhUquQpzWijittdvneX1eONsaA+HF\n6W6CUaVHgjYOVq0l7GteookgCOinT0Q/fSJuSpkEURRR0VQVWN9SXF+CvMr9yKvcD8AfXLLN5xbn\nWrWhO32+N3FEhSSHtZEu1ubKVNqasezNPZDJBCxbNB5xsdpe/f6si3RFsjY+0YfKpupAaCm2n+iy\npseqMSMrNtO/zsWcCYvGHJF+Apz6CYpvbOlibaSLtbly2w+V482NBcjsb8JT94+BXNZ7ayhYF+mS\nUm18og8VTVXt61v8ZxY1dzqF3aqxBBbnZpszYdbEhq1vnPohIoqwG0ckIb/Uhrxj1Vj/3UnMuSkj\n0l2iPkYmyNDfkIT+hiTcknojfKIP5Y2VKGofbSmuP4FdFXuxq2IvACBOaw3smpttzkSsOiYi/WZQ\nISIKA0EQ8MAdOThR7sBnO05iaLoZOemRG2onkgkypBiTkWJMxq2pk+ETfTjbWBE4Ffp4fSl2VOzB\njoo9AIBUY388nvuLyzq1vDcwqBARhYlOo8DPZw7DX97Zj39+ehTLHp4Ag1YZ6W4RAfAHl1Rjf6Qa\n++PWtJvgE30401geOBXav4Fe+FeLMKgQEYVRZv8Y/GTyQHz47Qm8ufEYHpszQrKnjFLfJhNkSDOm\nIM2YgmlpUyLXj4i1TETUR02/Lh05abE4UFyLbQfORro7RJLGoEJEFGYymYBHfjwMBq0Sq786jjPV\njZc+iKiPYlAhIooAs1GNh6cPgcfrw6sb8uFu80a6S0SSxKBCRBQho7PiMHVMCs7WNmHN18cj3R0i\nSWJQISKKoHtvzURKvB5bD5zFvsKaSHeHSHIYVIiIIkipkOPns4ZDpZDhrU3HYHN0f9Vhor6GQYWI\nKML6x+kxb1oWmlo8+McnR+HzSfbKJkRhx6BCRCQBU0YlY+zgeBSV1ePTnScj3R0iyQjZhm9r167F\nhg0bAvePHDmC1atXY+nSpQCAwYMHY9myZaFqnogoqgiCgIfuykFphQPrvyvFkHQzslLCd1E4IqkK\ny9WT8/LysGnTJhw/fhxPPvkkRo4ciSeeeAIzZ87ElCkX3+2OV0/um1gb6WJtQq+orB7L39sPi1GN\npQ9PgF5z6S32WRfpYm16prurJ4dl6mfFihV45JFHcPbsWYwcORIAcMstt2Dnzp3haJ6IKGpkp8Zi\n5g0DUedoxcrNhQjD35JEkhbya/0cOnQISUlJkMvlMJlMgcetVitqaro/Fc9s1kGhkIesb90lOIos\n1ka6WJvQWzRzOI6XO7C3oBoHRiTjjuvSL3kM6yJdrM3VCXlQ+eCDDzB79uwLHu/JXwl2e3MougSA\nw3FSxtpIF2sTPg/dMRhL3sjDPz46hH4xaiTH6S/6XNZFulibnono1M/u3buRm5sLi8WC+vr6wONV\nVVVISEgIdfNERFHJGqPBQ3flwO3x4ZX1+WjzcIt96ptCGlSqqqqg1+uhUqmgVCqRkZGBvXv3AgC2\nbNmCyZMnh7J5IqKoNi4nATePTsaZmkas3VoS6e4QRURIp35qampgsVgC9xcvXoxnnnkGPp8Po0aN\nwqRJk0LZPBFR1Js7NQtFZxrw5b4zGDrAgtFZcZHuElFYheX05CvF05P7JtZGulibyCirbsSfVu6F\nRiXHsocnwGxUd/l31kW6WJueifjpyUREdOVSEwyYe+sgNLra8Nqn3GKf+hYGFSKiKHDrmP4YPSgO\nx07ZsWn3qUh3hyhsGFSIiKKAIAhYND0HsQYVPvq2FCVnGyLdJaKwYFAhIooSRp0Kj/x4GERRxKsb\n8tHc4ol0l4hCjkGFiCiKDEk3Y8akdNQ2tGDVFm6xT9c+BhUioigz84aByEw2YffRKuw4Uhnp7hCF\nVMi30Cciot6lkMvw7zOHYembeXhnSxHGj0iGKtKd6qNEUYSzuQ11jhbUNbTA5mhBnaO1/bYFCqUc\nRo0CFpMGFqMaZqMaFpMG5vavFXKOF1wKgwoRURSKj9XiwTtz8Mr6fPzXyj2YOCQBMXoVYvQqmPQq\nxBjU0GsUEAQh0l2Nau42L2zOVtQ5WmBr8IcPm6P9fnso8Xh9QY9VyGUQBKDNE/zfAcCkV/nDi1EN\ni1EDs0kdCDRmkwZmgxpKRd8OMwwqRERRasKQRBw9ace3P5TjZIXjgn+XywSYOoJLkNtzX6uhVcv7\nXKgRRRGO5jZ/4AgyGmJztMDR3HbR4006JVLi9bCaNLCYNLCa/KMl1hgNrCYNjDol4uONKD1tg83R\nCruzFTZni//W0Qq7swU2ZyvKa5twqvLim8KZdEqYjRpYTF1HZCx9JMxwZ1qSHNZGulgb6RFFEfUt\nXpSW2dHQ5IajyY2GJjcaGlvhaHajodH/mLubv+oB/1//MXoVYgwqmHT+2+DhRg21Sh6mn+7qXO1o\nSCB4mPwhwdophJiNaqiUl/5/6Ml7RhRFNLra2oNMK+wOf4DxB5qWwOPdjcwYdUr/iIxRHRiV6bjf\nEXCUCunWrbudaTmiQkQUxQRBQHaaGWbtxT/ORVFEi9sbCDGBMNPkhqOp1R9mmv33T1U64b3Ezrdq\npdwfXgwqxOjOSyCrsgAACJVJREFUuz1vxCZUvxwvNhpS12k0xHk5oyExmi7BxKhThm2ESRAEGHUq\nGHUqpCUG/4UtiiKaWjxdgovd2QK7w/+1zdmKiromnKq6eCgyaJWwmC4MMB1TTmZDz8JXuDGoEBFd\n4wRBgFatgFatQKJF1+1zRVFEc6sHDY3nh5rWc0Gn0Y2GZjdOnHXAd4lBea1acZHppvbRm/ZRGqNO\n2WVhabDRkK4jIpceDUmJNwRGQAIjIpcxGiIlgiDAoFXCoFVeMsycPxLTeYSm0taM01WNF23HoFUG\nXfRrMWmQbNUhxqC+6LGhwqBCREQBgiBAr1FCr1EiOU7f7XN9Pv+URdcw04aGptbzpqHcqLQ1X7Lt\njl/ETS1tPRsNCYSQyI2GSEnnMJOaYAj6nI4g6h+JaQ8wjnNrZ+zOVlTZXThdfWGYkcsEPP/YDTDp\nwnuOGYMKERFdEVmnxbopl3iux+uDs/n8UHNu1Kbja2dzG/QaBVITDBeuD4nS0RAp6RxEU7oJM65W\nj39KqX3Rr93ZCrlcBoNWGeYeM6gQEVEYKOSywDQCSZsgCNBplNBplEiJDx5mwunaPZ+JiIiIoh6D\nChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMK\nERERSRaDChEREUkWgwoRERFJFoMKERERSZYgiqIY6U4QERERBcMRFSIiIpIsBhUiIiKSLAYVIiIi\nkiwGFSIiIpIsBhUiIiKSLAYVIiIikqw+GVSeffZZzJ07F/PmzcOhQ4ci3R3q5LnnnsPcuXNx9913\nY8uWLZHuDnXS0tKCadOm4cMPP4x0V6iTDRs2YObMmZgzZw62bdsW6e5Qu6amJjz22GNYuHAh5s2b\nh+3bt0e6S1FLEekOhFteXh5OnTqFNWvWoKSkBIsXL8aaNWsi3S0CsGvXLhQXF2PNmjWw2+2YPXs2\nbr/99kh3i9q9/PLLiImJiXQ3qBO73Y4VK1Zg3bp1aG5uxosvvoibb7450t0iAB999BEGDhyIJ554\nAlVVVXjwwQexefPmSHcrKvW5oLJz505MmzYNAJCZmYmGhgY0NjbCYDBEuGc0fvx4jBw5EgBgMpng\ncrng9Xohl8sj3DMqKSnB8ePH+UtQYnbu3Inrr78eBoMBBoMBf/rTnyLdJWpnNptRWFgIAHA4HDCb\nzRHuUfTqc1M/tbW1XV4wFosFNTU1EewRdZDL5dDpdACADz74ADfddBNDikQsX74cTz31VKS7Qec5\nc+YMWlpa8Itf/ALz58/Hzp07I90lajdjxgyUl5fjtttuw4IFC/Db3/420l2KWn1uROV8vIKA9Hz5\n5Zf44IMP8MYbb0S6KwTg448/xujRo5GamhrprlAQ9fX1eOmll1BeXo4HHngAW7duhSAIke5Wn7d+\n/XokJyfj9ddfR0FBARYvXsz1XVeozwWVhIQE1NbWBu5XV1cjPj4+gj2izrZv345XXnkFr732GoxG\nY6S7QwC2bduGsrIybNu2DZWVlVCpVOjXrx8mTZoU6a71eVarFbm5uVAoFEhLS4Ner4fNZoPVao10\n1/q8/fv348YbbwQA5OTkoLq6mlPZV6jPTf3ccMMN+PzzzwEA+fn5SEhI4PoUiXA6nXjuuefw6quv\nIjY2NtLdoXYvvPAC1q1bh3/961+455578OijjzKkSMSNN96IXbt2wefzwW63o7m5mWshJCI9PR0/\n/PADAODs2bPQ6/UMKVeoz42ojBkzBsOGDcO8efMgCAKWLFkS6S5Ru40bN8Jut+Pxxx8PPLZ8+XIk\nJydHsFdE0pWYmIg77rgD9957LwDgD3/4A2SyPvf3pyTNnTsXixcvxoIFC+DxeLB06dJIdylqCSIX\naRAREZFEMXoTERGRZDGoEBERkWQxqBAREZFkMagQERGRZDGoEBERkWQxqBBRrzhz5gyGDx+OhQsX\nBq4Y+8QTT8DhcPT4eyxcuBBer7fHz7/vvvuwe/fuK+kuEUUJBhUi6jUWiwWrVq3CqlWr8P777yMh\nIQEvv/xyj49ftWoVN8Uioi763IZvRBQ+48ePx5o1a1BQUIDly5fD4/Ggra0NzzzzDIYOHYqFCxci\nJycHx44dw8qVKzF06FDk5+fD7Xbj6aefRmVlJTweD2bNmoX58+fD5XLh17/+Nex2O9LT09Ha2goA\nqKqqwm9+8xsAQEtLC+bOnYuf/vSnkfzRiaiXMKgQUUh4vV588cUXGDt2LJ588kmsWLECaWlpF1yg\nTafT4Z133uly7KpVq2AymfD888+jpaUF06dPx+TJk7Fjxw5oNBqsWbMG1dXVmDp1KgBg06ZNyMjI\nwLJly9Da2oq1a9eG/eclotBgUCGiXmOz2bBw4UIAgM/nw7hx43D33Xfj73//O37/+98HntfY2Aif\nzwfAf1mL8/3www+YM2cOAECj0WD48OHIz89HUVERxo4dC8B/gdGMjAwAwOTJk/Hee+/hqaeewpQp\nUzB37tyQ/pxEFD4MKkTUazrWqHTmdDqhVCoveLyDUqm84DFBELrcF0URgiBAFMUu17LpCDuZmZn4\n7LPPsGfPHmzevBkrV67E+++/f7U/DhFJABfTElFIGY1GpKSk4JtvvgEAlJaW4qWXXur2mFGjRmH7\n9u0AgObmZuTn52PYsGHIzMzEgQMHAAAVFRUoLS0FAHzyySc4fPgwJk2ahCVLlqCiogIejyeEPxUR\nhQtHVIgo5JYvX44///nP+Mc//gGPx4Onnnqq2+cvXLgQTz/9NO6//3643W48+uijSElJwaxZs/D1\n119j/vz5SElJwYgRIwAAgwYNwpIlS6BSqSCKIh555BEoFPx4I7oW8OrJREREJFmc+iEiIiLJYlAh\nIiIiyWJQISIiIsliUCEiIiLJYlAhIiIiyWJQISIiIsliUCEiIiLJYlAhIiIiyfr/1L7e58979uEA\nAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "f85b317jlPqH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "2a892b5f-5866-4f7e-d7a3-29d646345533"
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 189.20\n",
+            "  period 01 : 117.74\n",
+            "  period 02 : 108.79\n",
+            "  period 03 : 97.80\n",
+            "  period 04 : 81.02\n",
+            "  period 05 : 73.38\n",
+            "  period 06 : 71.23\n",
+            "  period 07 : 70.65\n",
+            "  period 08 : 70.49\n",
+            "  period 09 : 69.71\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.71\n",
+            "Final RMSE (on validation data): 70.70\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AERERkXpHAUZERKSBWbPmxxq97o03/kZqaspltz/xxKO1VVKtU4ARERFpQNLSUlm5cnmN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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "N31ydESAlPqJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 376
+        },
+        "outputId": "73df22fc-4995-460e-d793-f0a73af58e5a"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.ylabel(\"RMSE\")\n",
+        "plt.xlabel(\"Periods\")\n",
+        "plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "plt.plot(adagrad_training_losses, label='Adagrad training')\n",
+        "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n",
+        "plt.plot(adam_training_losses, label='Adam training')\n",
+        "plt.plot(adam_validation_losses, label='Adam validation')\n",
+        "_ = plt.legend()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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a2hRfXz8A/Pz8ycs7zfHjcXTuHAlAv34D2Llz+2WPjz2oyC+hWcdwdsSUEf9b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\nYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "FSPZIiYgyh93"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "X1QcIeiKyni4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, let's try Adagrad."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ntn4jJxnypGZ",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "5JUsCdRRyso3"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's try Adam."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "lZB8k0upyuY8",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "twYgC8FGyxm6"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's print a graph of loss metrics side by side."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8RHIUEfqyzW0",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.ylabel(\"RMSE\")\n",
+        "plt.xlabel(\"Periods\")\n",
+        "plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "plt.plot(adagrad_training_losses, label='Adagrad training')\n",
+        "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n",
+        "plt.plot(adam_training_losses, label='Adam training')\n",
+        "plt.plot(adam_validation_losses, label='Adam validation')\n",
+        "_ = plt.legend()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "UySPl7CAQ28C"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Explore Alternate Normalization Methods\n",
+        "\n",
+        "**Try alternate normalizations for various features to further improve performance.**\n",
+        "\n",
+        "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n",
+        "\n",
+        "For example, many features have a median of `-0.8` or so, rather than `0.0`."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "QWmm_6CGKxlH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 715
+        },
+        "outputId": "682d3f4c-db02-433d-9ede-908746ae6716"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1296x864 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Xx9jgEMHKxlJ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We might be able to do better by choosing additional ways to transform these features.\n",
+        "\n",
+        "For example, a log scaling might help some features.  Or clipping extreme values may make the remainder of the scale more informative."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "baKZa6MEKxlK",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def log_normalize(series):\n",
+        "  return series.apply(lambda x:math.log(x+1.0))\n",
+        "\n",
+        "def clip(series, clip_to_min, clip_to_max):\n",
+        "  return series.apply(lambda x:(\n",
+        "    min(max(x, clip_to_min), clip_to_max)))\n",
+        "\n",
+        "def z_score_normalize(series):\n",
+        "  mean = series.mean()\n",
+        "  std_dv = series.std()\n",
+        "  return series.apply(lambda x:(x - mean) / std_dv)\n",
+        "\n",
+        "def binary_threshold(series, threshold):\n",
+        "  return series.apply(lambda x:(1 if x > threshold else 0))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-wCCq_ClKxlO"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n",
+        "\n",
+        "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8ToG-mLfMO9P",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "88517592-3515-442d-f531-0b5c99e74b19"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "\n",
+        "  processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n",
+        "  \n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "\n",
+        "  processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n",
+        "\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n",
+        "    steps=1000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 87.25\n",
+            "  period 01 : 76.35\n",
+            "  period 02 : 73.65\n",
+            "  period 03 : 72.65\n",
+            "  period 04 : 71.83\n",
+            "  period 05 : 70.83\n",
+            "  period 06 : 70.04\n",
+            "  period 07 : 70.18\n",
+            "  period 08 : 69.69\n",
+            "  period 09 : 68.86\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   68.86\n",
+            "Final RMSE (on validation data): 69.79\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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LQcZO+rXrjmHAz5m7HF2KiIhIk6UgYyd9O7bDONWSXMtJTpUXObocERGRJklB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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "GhFtWjQRzD2l"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "OMoIsUMmzK9b"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "These are only a few ways in which we could think about the data. Other transformations may work even better!\n",
+        "\n",
+        "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n",
+        "\n",
+        "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n",
+        "\n",
+        "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XDEYkPquzYCH",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "\n",
+        "  processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n",
+        "  \n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "\n",
+        "  processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n",
+        "\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n",
+        "    steps=1000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "b7atJTbzU9Ca"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Use only Latitude and Longitude Features\n",
+        "\n",
+        "**Train a NN model that uses only latitude and longitude as features.**\n",
+        "\n",
+        "Real estate people are fond of saying that location is the only important feature in housing price.\n",
+        "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n",
+        "\n",
+        "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n",
+        "\n",
+        "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5McjahpamOc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "5fc1e7e3-268f-4ce6-9379-50f050a545d1"
+      },
+      "cell_type": "code",
+      "source": [
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10, 5, 5, 5],\n",
+        "    training_examples=lll_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=lll_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 130.37\n",
+            "  period 01 : 107.41\n",
+            "  period 02 : 104.93\n",
+            "  period 03 : 103.91\n",
+            "  period 04 : 102.25\n",
+            "  period 05 : 101.98\n",
+            "  period 06 : 100.68\n",
+            "  period 07 : 100.26\n",
+            "  period 08 : 99.90\n",
+            "  period 09 : 99.82\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   99.82\n",
+            "Final RMSE (on validation data): 99.98\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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nVzNmaEKQKxIREekfNBPvKUgMT8BpicDi8rC7QCtTi4iI9BQFmFNgGAZZMWlY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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "P8BLQ7T71JWd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "1hwaFCE71OPZ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "It's a good idea to keep latitude and longitude normalized:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "djKtt4mz1ZEc",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10, 5, 5, 5],\n",
+        "    training_examples=lll_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=lll_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Dw2Mr9JZ1cRi"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb
new file mode 100644
index 0000000..c27f8d6
--- /dev/null
+++ b/intro_to_neural_nets.ipynb
@@ -0,0 +1,1177 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_neural_nets.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "O2q5RRCKqYaU",
+        "vvT2jDWjrKew"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/intro_to_neural_nets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "eV16J6oUY-HN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_wIcUFLSKNdx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n",
+        "  * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_ZZ7f7prKNdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n",
+        "\n",
+        "One important set of nonlinearities was around latitude and longitude, but there may be others.\n",
+        "\n",
+        "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "J2kqX6VZTHUy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's load and prepare the data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AGOM1TUiKNdz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2I8E2qhyKNd4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pQzcj2B1T5dA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1153
+        },
+        "outputId": "e32ad3c4-1b82-401a-8b23-a9fe0d900c06"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.5       2636.8           537.8   \n",
+              "std         2.1        2.0                12.6       2146.1           413.5   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1464.0           297.0   \n",
+              "50%        34.3     -118.5                29.0       2134.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3153.2           650.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1420.5       499.1            3.9               2.0  \n",
+              "std        1090.2       375.3            1.9               1.2  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         790.0       282.0            2.6               1.5  \n",
+              "50%        1163.0       409.0            3.5               1.9  \n",
+              "75%        1722.0       606.0            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2636.8</td>\n",
+              "      <td>537.8</td>\n",
+              "      <td>1420.5</td>\n",
+              "      <td>499.1</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2146.1</td>\n",
+              "      <td>413.5</td>\n",
+              "      <td>1090.2</td>\n",
+              "      <td>375.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1464.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2134.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1163.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3153.2</td>\n",
+              "      <td>650.0</td>\n",
+              "      <td>1722.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>16122.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.7       2660.2           543.2   \n",
+              "std         2.1        2.0                12.5       2259.2           440.1   \n",
+              "min        32.5     -124.3                 1.0         15.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1451.8           295.0   \n",
+              "50%        34.2     -118.5                29.0       2115.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3142.2           645.0   \n",
+              "max        41.9     -114.6                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1451.3       506.2            3.9               2.0  \n",
+              "std        1275.6       405.8            1.9               1.0  \n",
+              "min           8.0         3.0            0.5               0.1  \n",
+              "25%         789.0       280.0            2.6               1.5  \n",
+              "50%        1176.0       409.0            3.5               1.9  \n",
+              "75%        1718.0       603.0            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              29.4  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2660.2</td>\n",
+              "      <td>543.2</td>\n",
+              "      <td>1451.3</td>\n",
+              "      <td>506.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2259.2</td>\n",
+              "      <td>440.1</td>\n",
+              "      <td>1275.6</td>\n",
+              "      <td>405.8</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1451.8</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>789.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2115.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1176.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3142.2</td>\n",
+              "      <td>645.0</td>\n",
+              "      <td>1718.0</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>29.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.2\n",
+              "std                 116.4\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 180.5\n",
+              "75%                 265.0\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                207.5\n",
+              "std                 115.1\n",
+              "min                  15.0\n",
+              "25%                 122.0\n",
+              "50%                 179.8\n",
+              "75%                 265.1\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>122.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RWq0xecNKNeG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Building a Neural Network\n",
+        "\n",
+        "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+        "\n",
+        "Use **`hidden_units`** to define the structure of the NN.  The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it.  For example, consider the following assignment:\n",
+        "\n",
+        "`hidden_units=[3,10]`\n",
+        "\n",
+        "The preceding assignment specifies a neural net with two hidden layers:\n",
+        "\n",
+        "* The first hidden layer contains 3 nodes.\n",
+        "* The second hidden layer contains 10 nodes.\n",
+        "\n",
+        "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+        "\n",
+        "By default, all hidden layers will use ReLu activation and will be fully connected."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ni0S6zHcTb04",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zvCqgNdzpaFg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural net regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                             \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U52Ychv9KNeH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `DNNRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2QhdcCy-Y8QR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train a NN Model\n",
+        "\n",
+        "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+        "\n",
+        "Run the following block to train a NN model.  \n",
+        "\n",
+        "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good.  We'll aim to beat that.\n",
+        "\n",
+        "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+        "\n",
+        "Overfitting is a real potential hazard for NNs.  You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+        "\n",
+        "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+        "\n",
+        "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rXmtSW1yKNeK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 653
+        },
+        "outputId": "9e549c62-7ec5-4324-8121-9eb588c32138"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 159.41\n",
+            "  period 01 : 155.91\n",
+            "  period 02 : 154.10\n",
+            "  period 03 : 148.89\n",
+            "  period 04 : 146.00\n",
+            "  period 05 : 137.81\n",
+            "  period 06 : 128.35\n",
+            "  period 07 : 122.37\n",
+            "  period 08 : 115.46\n",
+            "  period 09 : 111.33\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   111.33\n",
+            "Final RMSE (on validation data): 110.58\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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xtNBqaO9uh/8VRY2Hsw1aM+e+VCvV5JcX1BQ3V4zcZJXWFDk5pbko1HRRrUbL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\nNGBERETE6WjAiIiIiNP5f6wxmq7e7qm7AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "O2q5RRCKqYaU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "j2Yd5VfrqcC3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IjkpSqmxqnSM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "c6diezCSeH4Y",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Evaluate on Test Data\n",
+        "\n",
+        "**Confirm that your validation performance results hold up on test data.**\n",
+        "\n",
+        "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n",
+        "\n",
+        "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "84dc6271-6a3e-4396-e5f7-9ddf1fb152d0"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+        "                                               test_targets[\"median_house_value\"], \n",
+        "                                               num_epochs=1, \n",
+        "                                               shuffle=False)\n",
+        "\n",
+        "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 110.03\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vvT2jDWjrKew",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FyDh7Qy6rQb0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+        "\n",
+        "Note that we don't have to randomize the test data, since we will use all records."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vhb0CtdvrWZx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+        "                                               test_targets[\"median_house_value\"], \n",
+        "                                               num_epochs=1, \n",
+        "                                               shuffle=False)\n",
+        "\n",
+        "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..11698cb
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1701 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_pandas.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "YHIWvc9Ms-Ll",
+        "TJffr5_Jwqvd"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/intro_to_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "rHLcriKWLRe4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to pandas"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "QvJBqX8_Bctk"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n",
+        "  * Access and manipulate data within a `DataFrame` and `Series`\n",
+        "  * Import CSV data into a *pandas* `DataFrame`\n",
+        "  * Reindex a `DataFrame` to shuffle data"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TIFJ83ZTBctl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n",
+        "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "s_JOISVgmn9v"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Basic Concepts\n",
+        "\n",
+        "The following line imports the *pandas* API and prints the API version:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "aSRYu62xUi3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "a2c3b7e0-8c74-4e44-c35a-b986e251ab0b"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import pandas as pd\n",
+        "pd.__version__"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "u'0.22.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "daQreKXIUslr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The primary data structures in *pandas* are implemented as two classes:\n",
+        "\n",
+        "  * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n",
+        "  * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
+        "\n",
+        "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fjnAk1xcU0yc"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to create a `Series` is to construct a `Series` object. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "DFZ42Uq7UFDj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "6a36738a-2d3d-4a22-c4c1-cd850d87a7b4"
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    San Francisco\n",
+              "1         San Jose\n",
+              "2       Sacramento\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "U5ouUp1cU6pC"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "avgr6GfiUh8t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "ae3ea68b-294c-4368-9aac-733ff74edaf8"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
+        "population = pd.Series([852469, 1015785, 485199])\n",
+        "\n",
+        "pd.DataFrame({ 'City name': city_names, 'Population': population })"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population\n",
+              "0  San Francisco      852469\n",
+              "1       San Jose     1015785\n",
+              "2     Sacramento      485199"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "oa5wfZT7VHJl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "av6RYOraVG1V",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "4c65cd6b-928c-4111-cbf0-dca714cce9ba"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.562108</td>\n",
+              "      <td>35.625225</td>\n",
+              "      <td>28.589353</td>\n",
+              "      <td>2643.664412</td>\n",
+              "      <td>539.410824</td>\n",
+              "      <td>1429.573941</td>\n",
+              "      <td>501.221941</td>\n",
+              "      <td>3.883578</td>\n",
+              "      <td>207300.912353</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.005166</td>\n",
+              "      <td>2.137340</td>\n",
+              "      <td>12.586937</td>\n",
+              "      <td>2179.947071</td>\n",
+              "      <td>421.499452</td>\n",
+              "      <td>1147.852959</td>\n",
+              "      <td>384.520841</td>\n",
+              "      <td>1.908157</td>\n",
+              "      <td>115983.764387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.350000</td>\n",
+              "      <td>32.540000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.499900</td>\n",
+              "      <td>14999.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.790000</td>\n",
+              "      <td>33.930000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>1462.000000</td>\n",
+              "      <td>297.000000</td>\n",
+              "      <td>790.000000</td>\n",
+              "      <td>282.000000</td>\n",
+              "      <td>2.566375</td>\n",
+              "      <td>119400.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.490000</td>\n",
+              "      <td>34.250000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>2127.000000</td>\n",
+              "      <td>434.000000</td>\n",
+              "      <td>1167.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>3.544600</td>\n",
+              "      <td>180400.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.000000</td>\n",
+              "      <td>37.720000</td>\n",
+              "      <td>37.000000</td>\n",
+              "      <td>3151.250000</td>\n",
+              "      <td>648.250000</td>\n",
+              "      <td>1721.000000</td>\n",
+              "      <td>605.250000</td>\n",
+              "      <td>4.767000</td>\n",
+              "      <td>265000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.310000</td>\n",
+              "      <td>41.950000</td>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>37937.000000</td>\n",
+              "      <td>6445.000000</td>\n",
+              "      <td>35682.000000</td>\n",
+              "      <td>6082.000000</td>\n",
+              "      <td>15.000100</td>\n",
+              "      <td>500001.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+              "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
+              "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
+              "std        2.005166      2.137340           12.586937   2179.947071   \n",
+              "min     -124.350000     32.540000            1.000000      2.000000   \n",
+              "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
+              "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
+              "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
+              "max     -114.310000     41.950000           52.000000  37937.000000   \n",
+              "\n",
+              "       total_bedrooms    population    households  median_income  \\\n",
+              "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
+              "mean       539.410824   1429.573941    501.221941       3.883578   \n",
+              "std        421.499452   1147.852959    384.520841       1.908157   \n",
+              "min          1.000000      3.000000      1.000000       0.499900   \n",
+              "25%        297.000000    790.000000    282.000000       2.566375   \n",
+              "50%        434.000000   1167.000000    409.000000       3.544600   \n",
+              "75%        648.250000   1721.000000    605.250000       4.767000   \n",
+              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+              "\n",
+              "       median_house_value  \n",
+              "count        17000.000000  \n",
+              "mean        207300.912353  \n",
+              "std         115983.764387  \n",
+              "min          14999.000000  \n",
+              "25%         119400.000000  \n",
+              "50%         180400.000000  \n",
+              "75%         265000.000000  \n",
+              "max         500001.000000  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "WrkBjfz5kEQu"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "s3ND3bgOkB5k",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "05e1d093-d22c-4370-d409-07726a08c1b1"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-114.31</td>\n",
+              "      <td>34.19</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5612.0</td>\n",
+              "      <td>1283.0</td>\n",
+              "      <td>1015.0</td>\n",
+              "      <td>472.0</td>\n",
+              "      <td>1.4936</td>\n",
+              "      <td>66900.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-114.47</td>\n",
+              "      <td>34.40</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>7650.0</td>\n",
+              "      <td>1901.0</td>\n",
+              "      <td>1129.0</td>\n",
+              "      <td>463.0</td>\n",
+              "      <td>1.8200</td>\n",
+              "      <td>80100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>-114.56</td>\n",
+              "      <td>33.69</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>720.0</td>\n",
+              "      <td>174.0</td>\n",
+              "      <td>333.0</td>\n",
+              "      <td>117.0</td>\n",
+              "      <td>1.6509</td>\n",
+              "      <td>85700.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-114.57</td>\n",
+              "      <td>33.64</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>1501.0</td>\n",
+              "      <td>337.0</td>\n",
+              "      <td>515.0</td>\n",
+              "      <td>226.0</td>\n",
+              "      <td>3.1917</td>\n",
+              "      <td>73400.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-114.57</td>\n",
+              "      <td>33.57</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1454.0</td>\n",
+              "      <td>326.0</td>\n",
+              "      <td>624.0</td>\n",
+              "      <td>262.0</td>\n",
+              "      <td>1.9250</td>\n",
+              "      <td>65500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
+              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
+              "2    -114.56     33.69                17.0        720.0           174.0   \n",
+              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
+              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
+              "\n",
+              "   population  households  median_income  median_house_value  \n",
+              "0      1015.0       472.0         1.4936             66900.0  \n",
+              "1      1129.0       463.0         1.8200             80100.0  \n",
+              "2       333.0       117.0         1.6509             85700.0  \n",
+              "3       515.0       226.0         3.1917             73400.0  \n",
+              "4       624.0       262.0         1.9250             65500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "w9-Es5Y6laGd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "nqndFVXVlbPN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 395
+        },
+        "outputId": "19f3cdd5-808f-44dc-963b-37d096013dfc"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.hist('housing_median_age')"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f40f9116950>]],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wQNnZ2ZKkvLw89evXryGOCQCAn4Uaz6g//PBDFRcXa8aMGb5to0aN0owZM9SmTRuFhIRo\n6dKlCg4O1syZM5WSkiKLxaLU1FTZ7XYlJCRoz549SkpKks1mU3p6eqMeEAAArYnFW5s3jZuYv5cj\nTLuE0dIwv7pjdvXj7/wmpe9sxNU0jA1pMU2yH/7s1Y9p86vXpW8AANB8CDUAAAYj1AAAGIxQAwBg\nMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYj1AAA\nGIxQAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYLbO4FAA1lUvrO5l5CtTakxTT3\nEgC0QJxRAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDB\nCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAbj91EDTcT035ct8TuzARNxRg0AgMFqdUa9fPly7du3\nT5cvX9bkyZMVFRWl2bNnq7KyUpGRkVqxYoVsNpuysrK0adMmBQQEaOzYsRozZowqKiqUlpamo0eP\nymq1aunSperSpUtjHxcAAK1CjaH+4osv9M033ygzM1PFxcV65JFH1L9/fyUnJ2v48OF66aWX5HQ6\nlZiYqDVr1sjpdCooKEijR49WXFyc8vLyFBoaqoyMDO3evVsZGRlauXJlUxwbAAAtXo2Xvu+55x69\n/PLLkqTQ0FCVlZWpoKBAsbGxkqQhQ4YoPz9f+/fvV1RUlOx2u4KDg9WnTx+5XC7l5+crLi5OkhQd\nHS2Xy9WIhwMAQOtS4xm11WpVSEiIJMnpdGrw4MHavXu3bDabJCkiIkJut1sej0fh4eG+54WHh1+1\nPSAgQBaLRZcuXfI9/1o6dAhRYKDVrwOJjLT79XhUxfwgNc+fg9b2Z68pj6e1za6ptZT51fqnvnfs\n2CGn06kNGzZo6NChvu1er/eaj/d3+5WKi0truyxJPw7b7T7n13PwP8wPP2nqPwet8c9eUx1Pa5xd\nUzJtftX9o6FWP/X92Wefae3atVq3bp3sdrtCQkJUXl4uSTpx4oQcDoccDoc8Ho/vOSdPnvRtd7vd\nkqSKigp5vd5qz6YBAMD/1Bjqc+fOafny5Xr99dcVFhYm6cf3mnNyciRJubm5GjRokHr16qXCwkKV\nlJTowoULcrlc6tu3rwYMGKDs7GxJUl5envr169eIhwMAQOtS46XvDz/8UMXFxZoxY4ZvW3p6uhYs\nWKDMzEx17txZiYmJCgoK0syZM5WSkiKLxaLU1FTZ7XYlJCRoz549SkpKks1mU3p6eqMeEAAArUmN\noX700Uf16KOPXrX9rbfeumpbfHy84uPjq2z76bPTAADAf9xCFIBPS7jNKfBzwy1EAQAwGKEGAMBg\nhBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGHcmQ61wxyoAaB6cUQMAYDBCDQCA\nwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMA\nYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABgssLkXAADAlSal72zuJdRoQ1pM\nk+2LM2oAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYLX6HPXXX3+tqVOnauLEiRo/\nfrzS0tJ08OBBhYWFSZJSUlL0wAMPKCsrS5s2bVJAQIDGjh2rMWPGqKKiQmlpaTp69KisVquWLl2q\nLl26NOpBAUBz4TPAaGg1hrq0tFRLlixR//79q2x/+umnNWTIkCqPW7NmjZxOp4KCgjR69GjFxcUp\nLy9PoaGhysjI0O7du5WRkaGVK1c2/JEAANAK1Xjp22azad26dXI4HNU+bv/+/YqKipLdbldwcLD6\n9Okjl8ul/Px8xcXFSZKio6PlcrkaZuUAAPwM1BjqwMBABQcHX7V9y5YtmjBhgp566imdPn1aHo9H\n4eHhvu+Hh4fL7XZX2R4QECCLxaJLly414CEAANB61ele3w8//LDCwsLUo0cPvfHGG3rllVd09913\nV3mM1+u95nOvt/1KHTqEKDDQ6teaIiPtfj0eVTE/4OeDv+/115QzrFOor3y/OiYmRosXL9awYcPk\n8Xh820+ePKnevXvL4XDI7Xare/fuqqiokNfrlc1mq/b1i4tL/VpPZKRdbvc5/w4CPswP+Hnh73v9\nNfQMqwt/nT6e9eSTT+rw4cOSpIKCAnXt2lW9evVSYWGhSkpKdOHCBblcLvXt21cDBgxQdna2JCkv\nL0/9+vWryy4BAPhZqvGM+sCBA1q2bJm+//57BQYGKicnR+PHj9eMGTPUpk0bhYSEaOnSpQoODtbM\nmTOVkpIii8Wi1NRU2e12JSQkaM+ePUpKSpLNZlN6enpTHBcAAK1CjaHu2bOnNm/efNX2YcOGXbUt\nPj5e8fHxVbb99NlpAADgP+5MBgCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMA\nYDBCDQCAwQg1AAAGI9QAABiMUAMAYLA6/T5qAEDLNSl9Z3MvAX7gjBoAAIMRagAADEaoAQAwGKEG\nAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEao\nAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMR\nagAADFarUH/99dd68MEHtWXLFknSsWPH9Lvf/U7JycmaPn26Ll26JEnKysrSb37zG40ZM0bvvfee\nJKmiokIzZ85UUlKSxo8fr8OHDzfSoQAA0PrUGOrS0lItWbJE/fv3921btWqVkpOT9fbbb+uWW26R\n0+lUaWmp1qxZo40bN2rz5s3atGmTzpw5o+3btys0NFTvvPOOpkyZooyMjEY9IAAAWpMaQ22z2bRu\n3To5HA7ftoKCAsXGxkqShgwZovz8fO3fv19RUVGy2+0KDg5Wnz595HK5lJ+fr7i4OElSdHS0XC5X\nIx0KAACtT42hDgwMVHBwcJVtZWVlstlskqSIiAi53W55PB6Fh4f7HhMeHn7V9oCAAFksFt+lcgAA\nUL3A+r6A1+ttkO1X6tAhRIGBVr/WERlp9+vxqIr5AUDtNeX/M+sU6pCQEJWXlys4OFgnTpyQw+GQ\nw+GQx+PxPebkyZPq3bu3HA6H3G63unfvroqKCnm9Xt/Z+PUUF5f6tZ7ISLvc7nN1ORSI+QGAvxr6\n/5nVhb9OH8+Kjo5WTk6OJCk3N1eDBg1Sr169VFhYqJKSEl24cEEul0t9+/bVgAEDlJ2dLUnKy8tT\nv3796rJLAAB+lmo8oz5w4ICWLVum77//XoGBgcrJydGLL76otLQ0ZWZmqnPnzkpMTFRQUJBmzpyp\nlJQUWSwWpaamym63KyEhQXv27FFSUpJsNpvS09Ob4rgAAGgVLN7avGncxPy9pMCl2/qpzfwmpe9s\notUAgPk2pMU06Os1+KVvAADQNOr9U99oGJyxAgCuhTNqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAM\nRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAA\ngxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMRqgBADAYoQYA\nwGCEGgAAgxFqAAAMRqgBADAYoQYAwGCEGgAAgxFqAAAMFtjcC2gKk9J3NvcSAACoE86oAQAwGKEG\nAMBghBoAAIMRagAADFanHyYrKCjQ9OnT1bVrV0lSt27d9Pjjj2v27NmqrKxUZGSkVqxYIZvNpqys\nLG3atEkBAQEaO3asxowZ06AHAABAa1bnn/q+9957tWrVKt/Xc+fOVXJysoYPH66XXnpJTqdTiYmJ\nWrNmjZxOp4KCgjR69GjFxcUpLCysQRYPAEBr12CXvgsKChQbGytJGjJkiPLz87V//35FRUXJbrcr\nODhYffr0kcvlaqhdAgDQ6tX5jPrQoUOaMmWKzp49q2nTpqmsrEw2m02SFBERIbfbLY/Ho/DwcN9z\nwsPD5Xa7a3ztDh1CFBho9Ws9kZF2/w4AAIA6asrm1CnUt956q6ZNm6bhw4fr8OHDmjBhgiorK33f\n93q913ze9bb//4qLS/1aT2SkXW73Ob+eAwBAXTV0c6oLf50ufXfq1EkJCQmyWCy6+eab1bFjR509\ne1bl5eWSpBMnTsjhcMjhcMjj8fied/LkSTkcjrrsEgCAn6U6hTorK0tvvvmmJMntduvUqVMaNWqU\ncnJyJEm5ubkaNGiQevXqpcLCQpWUlOjChQtyuVzq27dvw60eAIBWrk6XvmNiYvTMM8/ok08+UUVF\nhRYvXqwePXpozpw5yszMVOfOnZWYmKigoCDNnDlTKSkpslgsSk1Nld3Oe8kAANSWxVvbN46bkL/X\n/mt6j5pfygEAaEgb0mIa9PUa/D1qAADQNAg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiM\nUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAG\nI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCA\nwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABgssCl2\n8sILL2j//v2yWCyaN2+e7rrrrqbYLQAALV6jh/rLL7/Ud999p8zMTBUVFWnevHnKzMxs7N0CANAq\nNPql7/z8fD344IOSpNtuu01nz57V+fPnG3u3AAC0Co0eao/How4dOvi+Dg8Pl9vtbuzdAgDQKjTJ\ne9RX8nq9NT4mMtLu9+tW95wPMh72+/UAADBBo59ROxwOeTwe39cnT55UZGRkY+8WAIBWodFDPWDA\nAOXk5EiSDh48KIfDoXbt2jX2bgEAaBUa/dJ3nz59dOedd2rcuHGyWCxatGhRY+8SAIBWw+KtzZvG\nAACgWXBnMgAADEaoAQAwWJN/PKuhcXtS/3399deaOnWqJk6cqPHjx+vYsWOaPXu2KisrFRkZqRUr\nVshmszX3Mo20fPly7du3T5cvX9bkyZMVFRXF7GqhrKxMaWlpOnXqlC5evKipU6eqe/fuzM5P5eXl\nGjFihKZOnar+/fszv1oqKCjQ9OnT1bVrV0lSt27d9Pjjj7eY+bXoM+orb0/6/PPP6/nnn2/uJRmv\ntLRUS5YsUf/+/X3bVq1apeTkZL399tu65ZZb5HQ6m3GF5vriiy/0zTffKDMzU+vXr9cLL7zA7Gop\nLy9PPXv21JYtW7Ry5Uqlp6czuzp47bXX1L59e0n8vfXXvffeq82bN2vz5s1auHBhi5pfiw41tyf1\nn81m07p16+RwOHzbCgoKFBsbK0kaMmSI8vPzm2t5Rrvnnnv08ssvS5JCQ0NVVlbG7GopISFBTzzx\nhCTp2LFj6tSpE7PzU1FRkQ4dOqQHHnhAEn9v66slza9Fh5rbk/ovMDBQwcHBVbaVlZX5LvlEREQw\nw+uwWq0KCQmRJDmdTg0ePJjZ+WncuHF65plnNG/ePGbnp2XLliktLc33NfPzz6FDhzRlyhQlJSXp\n888/b1Hza/HvUV+JT5rVHzOs2Y4dO+R0OrVhwwYNHTrUt53Z1ezdd9/VV199pVmzZlWZF7Or3l/+\n8hf17t1bXbp0ueb3mV/1br31Vk2bNk3Dhw/X4cOHNWHCBFVWVvq+b/r8WnSouT1pwwgJCVF5ebmC\ng4N14sSJKpfFUdVnn32mtWvXav369bLb7cyulg4cOKCIiAjdcMMN6tGjhyorK9W2bVtmV0u7du3S\n4cOHtWvXLh0/flw2m40/e37o1KmTEhISJEk333yzOnbsqMLCwhYzvxZ96ZvbkzaM6Oho3xxzc3M1\naNCgZl6Rmc6dO6fly5fr9ddfV1hYmCRmV1t79+7Vhg0bJP34llVpaSmz88PKlSu1detW/fnPf9aY\nMWM0depU5ueHrKwsvfnmm5Ikt9utU6dOadSoUS1mfi3+zmQvvvii9u7d67s9affu3Zt7SUY7cOCA\nli1bpu+//16BgYHq1KmTXnytKYqYAAAArElEQVTxRaWlpenixYvq3Lmzli5dqqCgoOZeqnEyMzO1\nevVq/fKXv/RtS09P14IFC5hdDcrLyzV//nwdO3ZM5eXlmjZtmnr27Kk5c+YwOz+tXr1aN954owYO\nHMj8aun8+fN65plnVFJSooqKCk2bNk09evRoMfNr8aEGAKA1a9GXvgEAaO0INQAABiPUAAAYjFAD\nAGAwQg0AgMEINQAABiPUAAAYjFADAGCw/wdkB5RjykY3PgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "XtYZ7114n3b-"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Accessing Data\n",
+        "\n",
+        "You can access `DataFrame` data using familiar Python dict/list operations:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "_TFm7-looBFF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101
+        },
+        "outputId": "659c8c07-aea3-4054-d877-f6c65a6e04c3"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n",
+        "print(type(cities['City name']))\n",
+        "cities['City name']"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.series.Series'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    San Francisco\n",
+              "1         San Jose\n",
+              "2       Sacramento\n",
+              "Name: City name, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "V5L6xacLoxyv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "87c97306-37a4-4141-f534-caab31e1cf98"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities['City name'][1]))\n",
+        "cities['City name'][1]"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<type 'str'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'San Jose'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "gcYX1tBPugZl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 123
+        },
+        "outputId": "453e0854-8f74-42fd-d25f-56a7f117820f"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities[0:2]))\n",
+        "cities[0:2]"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population\n",
+              "0  San Francisco      852469\n",
+              "1       San Jose     1015785"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "65g1ZdGVjXsQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "RM1iaD-ka3Y1"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Manipulating Data\n",
+        "\n",
+        "You may apply Python's basic arithmetic operations to `Series`. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XWmyCFJ5bOv-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "e20dc3b7-41af-4370-915a-343a1bfb0c1e"
+      },
+      "cell_type": "code",
+      "source": [
+        "population / 1000."
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0     852.469\n",
+              "1    1015.785\n",
+              "2     485.199\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TQzIVnbnmWGM"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "ko6pLK6JmkYP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "07e0034f-ed7d-4081-99fd-b2a59e9109cc"
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.log(population)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    13.655892\n",
+              "1    13.831172\n",
+              "2    13.092314\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "xmxFuQmurr6d"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n",
+        "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n",
+        "\n",
+        "The example below creates a new `Series` that indicates whether `population` is over one million:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Fc1DvPAbstjI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "a20e49b0-a848-42e1-9545-a47fa2813c16"
+      },
+      "cell_type": "code",
+      "source": [
+        "population.apply(lambda val: val > 1000000)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "ZeYYLoV9b9fB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "0gCEX99Hb8LR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "6676363b-5bc6-498b-ee8d-268f400cce2f"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n",
+        "cities['Population density'] = cities['Population'] / cities['Area square miles']\n",
+        "cities"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density\n",
+              "0  San Francisco      852469              46.87        18187.945381\n",
+              "1       San Jose     1015785             176.53         5754.177760\n",
+              "2     Sacramento      485199              97.92         4955.055147"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "6qh63m-ayb-c"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #1\n",
+        "\n",
+        "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
+        "\n",
+        "  * The city is named after a saint.\n",
+        "  * The city has an area greater than 50 square miles.\n",
+        "\n",
+        "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
+        "\n",
+        "**Hint:** \"San\" in Spanish means \"saint.\""
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "zCOn8ftSyddH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "859b0a79-f518-408b-a5b3-90a7ee81e38f"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+        "cities"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "0                       False  \n",
+              "1                        True  \n",
+              "2                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "YHIWvc9Ms-Ll"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5OlrqtdtCIb",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+        "cities"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "f-xAOJeMiXFB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Indexes\n",
+        "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n",
+        "\n",
+        "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "2684gsWNinq9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "9f63eccf-c006-41cb-8ea4-df11c103d258"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names.index"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "F_qPe2TBjfWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "151808c5-5255-47d8-d3b5-6b8a02d66771"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.index"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "hp2oWY9Slo_h"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "sN0zUzSAj-U1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "1445200c-8648-47e6-cf85-f34aa4b733e4"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([2, 0, 1])"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "2                       False  \n",
+              "0                       False  \n",
+              "1                        True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-GQFz8NZuS06"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
+        "Try running the following cell multiple times!"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "mF8GC0k8uYhz",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "cd6af676-4bf0-4f3e-f89b-5893a3d24f14"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex(np.random.permutation(cities.index))"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "1                        True  \n",
+              "0                       False  \n",
+              "2                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fSso35fQmGKb"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8UngIdVhz8C0"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #2\n",
+        "\n",
+        "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "PN55GrDX0jzO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        },
+        "outputId": "ab5fc842-9921-497a-bda6-e7945b494355"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469.0</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199.0</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco    852469.0              46.87        18187.945381   \n",
+              "4            NaN         NaN                NaN                 NaN   \n",
+              "5            NaN         NaN                NaN                 NaN   \n",
+              "2     Sacramento    485199.0              97.92         4955.055147   \n",
+              "\n",
+              "  Is wide and has saint name  \n",
+              "0                      False  \n",
+              "4                        NaN  \n",
+              "5                        NaN  \n",
+              "2                      False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TJffr5_Jwqvd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8oSvi2QWwuDH"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "yBdkucKCwy4x",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "2l82PhPbwz7g"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n",
+        "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n",
+        "in which the index values are browser names).\n",
+        "\n",
+        "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n",
+        "sanitizing the input."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb
new file mode 100644
index 0000000..4a42327
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1588 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "logistic_regression.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "dPpJUV862FYI",
+        "i2e3TlyL57Qs",
+        "wCugvl0JdWYL"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Logistic Regression"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LEAHZv4rIYHX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n",
+        "  * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CnkCZqdIIYHY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9pltCyy2K3dd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Frame the Problem as Binary Classification\n",
+        "\n",
+        "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n",
+        "\n",
+        "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "67IJwZX1Vvjt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and prepare the input features and targets."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fOlbcJ4EIYHd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "lTB73MNeIYHf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kPSqspaqIYHg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FwOYWmXqWA6D",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1153
+        },
+        "outputId": "bdcb181f-8f28-4edb-ed01-f5c0dfe40711"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2643.0           538.5   \n",
+              "std         2.1        2.0                12.5       2179.4           419.7   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1458.8           296.0   \n",
+              "50%        34.3     -118.5                29.0       2129.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3156.0           648.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1425.2       499.9            3.9               2.0  \n",
+              "std        1137.6       381.5            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.1  \n",
+              "25%         789.0       281.0            2.6               1.5  \n",
+              "50%        1168.0       409.0            3.5               1.9  \n",
+              "75%        1714.0       604.0            4.7               2.3  \n",
+              "max       35682.0      5189.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.0</td>\n",
+              "      <td>538.5</td>\n",
+              "      <td>1425.2</td>\n",
+              "      <td>499.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2179.4</td>\n",
+              "      <td>419.7</td>\n",
+              "      <td>1137.6</td>\n",
+              "      <td>381.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1458.8</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>789.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2129.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3156.0</td>\n",
+              "      <td>648.0</td>\n",
+              "      <td>1714.0</td>\n",
+              "      <td>604.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.5                28.5       2645.2           541.7   \n",
+              "std         2.1        2.0                12.7       2181.4           425.8   \n",
+              "min        32.5     -124.3                 1.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1471.8           298.8   \n",
+              "50%        34.2     -118.5                29.0       2124.5           430.5   \n",
+              "75%        37.7     -118.0                37.0       3140.2           651.0   \n",
+              "max        41.9     -114.6                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1440.1       504.4            3.9               2.0  \n",
+              "std        1172.1       391.7            1.9               1.2  \n",
+              "min           8.0         3.0            0.5               0.0  \n",
+              "25%         792.0       282.0            2.6               1.5  \n",
+              "50%        1164.0       408.0            3.5               2.0  \n",
+              "75%        1745.0       607.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2645.2</td>\n",
+              "      <td>541.7</td>\n",
+              "      <td>1440.1</td>\n",
+              "      <td>504.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.7</td>\n",
+              "      <td>2181.4</td>\n",
+              "      <td>425.8</td>\n",
+              "      <td>1172.1</td>\n",
+              "      <td>391.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1471.8</td>\n",
+              "      <td>298.8</td>\n",
+              "      <td>792.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2124.5</td>\n",
+              "      <td>430.5</td>\n",
+              "      <td>1164.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3140.2</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1745.0</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "uon1LB3A31VN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## How Would Linear Regression Fare?\n",
+        "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n",
+        "\n",
+        "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "smmUYRDtWOV_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "B5OwSrr1yIKD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "SE2-hq8PIYHz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_regressor_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    \n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "TDBD8xeeIYH2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "4617711d-a835-474e-8f2c-034dcea6a2b1"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_linear_regressor_model(\n",
+        "    learning_rate=0.000001,\n",
+        "    steps=200,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.45\n",
+            "  period 01 : 0.45\n",
+            "  period 02 : 0.44\n",
+            "  period 03 : 0.44\n",
+            "  period 04 : 0.44\n",
+            "  period 05 : 0.44\n",
+            "  period 06 : 0.44\n",
+            "  period 07 : 0.44\n",
+            "  period 08 : 0.44\n",
+            "  period 09 : 0.44\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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CCCFEJyABpwvo7+vI0/cE8uRdI+ntaU98agGr/hHH+9+mUlJR2+K6VmZWjPYM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\nRwghhBBW5/8HBatsa/ORjY0AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JjBZ_q7aD9gh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Can We Calculate LogLoss for These Predictions?\n",
+        "\n",
+        "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n",
+        "\n",
+        "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability.  For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n",
+        "\n",
+        "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily.  Remember, `LogLoss` is defined as:\n",
+        "\n",
+        "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n",
+        "\n",
+        "\n",
+        "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n",
+        "\n",
+        "Given the predictions and the targets, can we calculate `LogLoss`?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dPpJUV862FYI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to display the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kXFQ5uig2RoP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                  validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "\n",
+        "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "\n",
+        "_ = plt.hist(validation_predictions)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "rYpy336F9wBg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n",
+        "\n",
+        "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n",
+        "\n",
+        "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JElcb--E9wBm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)  \n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VM0wmnFUIYH9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "66fb32ca-eb26-42f6-9e18-7d50d2137800"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.60\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.56\n",
+            "  period 03 : 0.55\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.53\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RXt46/37KyEwzeLlYM6ZvENsu5YBvEquSf+DPfWYZuiwhhBBG7J7AO64bRdH33EzdugVQ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\nIiIiIU1hRkRERELa/wdEjxt01vDmuQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i2e3TlyL57Qs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see the solution.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5YxXd2hn6MuF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)  \n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UPM_T1FXsTaL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "71babf36-4ec8-41e1-f9d2-7441028ba5b8"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.60\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.56\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.56\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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8vLyr9lm0aBF33303S5cuRa/XM2nSJC5fvkxUVBT33nsvf/3rXw0Vr8OxNNcw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\nzAghhBDCrkkyI4QQQgi79v8D400FOnAF200AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-Xo83_aR6s_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n",
+        "\n",
+        "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n",
+        "\n",
+        "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "47xGS2uNIYIE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n",
+        "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n",
+        "obtain the true positive and false positive rates needed to plot a ROC curve."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PIdhwfgzIYII",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+        "\n",
+        "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+        "\n",
+        "**Verify if all metrics improve at the same time.**"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XKIqjsqcCaxO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 831
+        },
+        "outputId": "79a6d694-43f0-4775-b4c8-e4905c99d5f3"
+      },
+      "cell_type": "code",
+      "source": [
+        "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.60\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.56\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.54\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-21-0dccc448471d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m     validation_targets=validation_targets)\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mevaluation_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredict_validation_input_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"AUC on the validation set: %0.2f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mevaluation_metrics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'auc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'predict_validation_input_fn' is not defined"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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M4IMPlnPyZCRlZeWMGTOekJB7mD17Jr169SEiIpzs7GzefPMdPDw86v09pZi5TU52FoS2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\nKcyIiIiIR/s/R4sOTTVxKHoAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wCugvl0JdWYL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VHosS1g2aetf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One possible solution that works is to just train for longer, as long as we don't overfit. \n",
+        "\n",
+        "We can do this by increasing the number the steps, the batch size, or both.\n",
+        "\n",
+        "All metrics improve at the same time, so our loss metric is a good proxy\n",
+        "for both AUC and accuracy.\n",
+        "\n",
+        "Notice how it takes many, many more iterations just to squeeze a few more \n",
+        "units of AUC. This commonly happens. But often even this small gain is worth \n",
+        "the costs."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dWgTEYMddaA-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000003,\n",
+        "    steps=20000,\n",
+        "    batch_size=500,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qwWF5ceWeunl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb
new file mode 100644
index 0000000..50560ce
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2780 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "multi-class_classification_of_handwritten_digits.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "266KQvZoMxMv",
+        "6sfw3LH0Oycm"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/multi_class_classification_of_handwritten_digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mPa95uXvcpcn",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Classifying Handwritten Digits with Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Fdpn8b90u8Tp",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c7HLCm66Cs2p",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n",
+        "  * Compare the performance of the linear and neural network classification models\n",
+        "  * Visualize the weights of a neural-network hidden layer"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HSEh-gNdu8T0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2NMdE1b-7UIH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4LJ4SD8BWHeh",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "outputId": "40ec734a-0944-4178-e039-6ed362a22811"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import glob\n",
+        "import math\n",
+        "import os\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "mnist_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "# Use just the first 10,000 records for training/validation.\n",
+        "mnist_dataframe = mnist_dataframe.head(10000)\n",
+        "\n",
+        "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
+        "mnist_dataframe.head()"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3912</th>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3437</th>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9896</th>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6119</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9110</th>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 785 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      0    1    2    3    4    5    6    7    8    9   ...   775  776  777  \\\n",
+              "3912    8    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "3437    8    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "9896    6    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "6119    3    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "9110    2    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "3912    0    0    0    0    0    0    0  \n",
+              "3437    0    0    0    0    0    0    0  \n",
+              "9896    0    0    0    0    0    0    0  \n",
+              "6119    0    0    0    0    0    0    0  \n",
+              "9110    0    0    0    0    0    0    0  \n",
+              "\n",
+              "[5 rows x 785 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kg0-25p2mOi0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains '6', then a human rater interpreted the handwritten character as the digit '6'.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PQ7vuOwRCsZ1",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dghlqJPIu8UM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren't all 0.  For example, adjust the following text block to print out the values in column 72."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2ZkrL5MCqiJI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 402
+        },
+        "outputId": "d43a73c2-2507-4b41-ab93-951f616f5b83"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_dataframe.loc[:, 72:72]"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>72</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3912</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3437</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9896</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6119</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9110</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5752</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9794</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4431</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2799</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8826</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "3912   0\n",
+              "3437   0\n",
+              "9896   0\n",
+              "6119   0\n",
+              "9110   0\n",
+              "...   ..\n",
+              "5752   0\n",
+              "9794   0\n",
+              "4431   0\n",
+              "2799   0\n",
+              "8826   0\n",
+              "\n",
+              "[10000 rows x 1 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vLNg2VxqhUZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JfFWWvMWDFrR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def parse_labels_and_features(dataset):\n",
+        "  \"\"\"Extracts labels and features.\n",
+        "  \n",
+        "  This is a good place to scale or transform the features if needed.\n",
+        "  \n",
+        "  Args:\n",
+        "    dataset: A Pandas `Dataframe`, containing the label on the first column and\n",
+        "      monochrome pixel values on the remaining columns, in row major order.\n",
+        "  Returns:\n",
+        "    A `tuple` `(labels, features)`:\n",
+        "      labels: A Pandas `Series`.\n",
+        "      features: A Pandas `DataFrame`.\n",
+        "  \"\"\"\n",
+        "  labels = dataset[0]\n",
+        "\n",
+        "  # DataFrame.loc index ranges are inclusive at both ends.\n",
+        "  features = dataset.loc[:,1:784]\n",
+        "  # Scale the data to [0, 1] by dividing out the max value, 255.\n",
+        "  features = features / 255\n",
+        "\n",
+        "  return labels, features"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mFY_-7vZu8UU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 313
+        },
+        "outputId": "53f61dd3-30c5-4e11-8e61-18eb234fdd58"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.3    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 7500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4-Vgg-1zu8Ud",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 313
+        },
+        "outputId": "bbe9eb03-b7dc-4291-929b-4327af6e443a"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets, validation_examples = parse_labels_and_features(mnist_dataframe[7500:10000])\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.8    0.2    1.0    0.2    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 2500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wrnAI1v6u8Uh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Show a random example and its corresponding label."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s-euVJVtu8Ui",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        },
+        "outputId": "29bc92d9-0c8b-4324-a539-4402c5885448"
+      },
+      "cell_type": "code",
+      "source": [
+        "rand_example = np.random.choice(training_examples.index)\n",
+        "_, ax = plt.subplots()\n",
+        "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n",
+        "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n",
+        "ax.grid(False)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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bNW/ePO3atUt9fX2aPn26Dh06pJCQEH/PCgCOGTKWdXV1unbtmtxutzo7O7V27VolJiYq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\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ScmYX7xdZMXE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Build a Linear Model for MNIST\n",
+        "\n",
+        "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n",
+        "\n",
+        "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix).  The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n",
+        "\n",
+        "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cpoVC4TSdw5Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  \n",
+        "  # There are 784 pixels in each image.\n",
+        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMmL89yGeTfz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OeS47Bmn5Ms2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
+        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The training features.\n",
+        "    labels: The training labels.\n",
+        "    batch_size: Batch size to use during training.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns batches of training features and labels during\n",
+        "    training.\n",
+        "  \"\"\"\n",
+        "  def _input_fn(num_epochs=None, shuffle=True):\n",
+        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
+        "    # gets a good sampling of data, even when number of steps is small, we \n",
+        "    # shuffle all the data before creating the Dataset object\n",
+        "    idx = np.random.permutation(features.index)\n",
+        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
+        "    raw_targets = np.array(labels[idx])\n",
+        "   \n",
+        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8zoGWAoohrwS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_predict_input_fn(features, labels, batch_size):\n",
+        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The features to base predictions on.\n",
+        "    labels: The labels of the prediction examples.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns features and labels for predictions.\n",
+        "  \"\"\"\n",
+        "  def _input_fn():\n",
+        "    raw_features = {\"pixels\": features.values}\n",
+        "    raw_targets = np.array(labels)\n",
+        "    \n",
+        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size)\n",
+        "    \n",
+        "        \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "G6DjSLZMu8Um",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, and a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `LinearClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create a LinearClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(),\n",
+        "      n_classes=10,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ItHIUyv2u8Ur",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n",
+        "\n",
+        "Stop if you get anything above about 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yaiIhIQqu8Uv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 970
+        },
+        "outputId": "18152b15-277e-4d2e-8a60-07edce90cc5d"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_linear_classification_model(\n",
+        "    learning_rate=0.03,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 11.54\n",
+            "  period 01 : 11.11\n",
+            "  period 02 : 9.20\n",
+            "  period 03 : 6.70\n",
+            "  period 04 : 6.51\n",
+            "  period 05 : 6.01\n",
+            "  period 06 : 5.76\n",
+            "  period 07 : 5.46\n",
+            "  period 08 : 5.37\n",
+            "  period 09 : 6.12\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.82\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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vrx7ZWWyO0oQQwuSGFNrr1q3jvffe44477uDOO+/k/fffJysr6/ov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\nrkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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JIiJSiCxDW3Z2dhg7dmyD57y8vODl5SVHOSIikyTn0FZcXByOHTsGQRAQEREBb29vw2sX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\nrkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "266KQvZoMxMv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lRWcn24DM3qa",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here is a set of parameters that should attain roughly 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TGlBMrUoM1K_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_linear_classification_model(\n",
+        "    learning_rate=0.03,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mk095OfpPdOx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Replace the Linear Classifier with a Neural Network\n",
+        "\n",
+        "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n",
+        "\n",
+        "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "IRFEwUb-mbn1",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, as well as a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `DNNClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  # Caution: input pipelines are reset with each call to train. \n",
+        "  # If the number of steps is small, your model may never see most of the data.  \n",
+        "  # So with multiple `.train` calls like this you may want to control the length \n",
+        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  # Create a DNNClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.DNNClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      n_classes=10,\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "LLWRAnMKmbn5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 970
+        },
+        "outputId": "ebf7ece1-8f26-4c7e-c4e9-9cd36818f9eb"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_nn_classification_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    hidden_units=[100, 100],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.77\n",
+            "  period 01 : 3.22\n",
+            "  period 02 : 2.62\n",
+            "  period 03 : 2.98\n",
+            "  period 04 : 2.71\n",
+            "  period 05 : 2.27\n",
+            "  period 06 : 2.00\n",
+            "  period 07 : 2.22\n",
+            "  period 08 : 1.99\n",
+            "  period 09 : 2.16\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.94\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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oqzHUizeARqvjhwMFrEs5TbtGR9RwT5bOH0OQj+keYej0Or4++R07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\nrkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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D0aNHN3jMy8sLXl5ecpQjIjJJWjm0xetIiIhUi0FCREQSsEdCRESSCBYMEiIikoA9EiIi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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TOfmiSvqu8U9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "evlB5ubzu8VJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 313
+        },
+        "outputId": "8bdb901c-93d4-4e12-e167-9a861ecc832e"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          1       2       3       4       5       6       7       8       9    \\\n",
+              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          10    ...       775     776     777     778     779     780     781  \\\n",
+              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          782     783     784  \n",
+              "count 10000.0 10000.0 10000.0  \n",
+              "mean      0.0     0.0     0.0  \n",
+              "std       0.0     0.0     0.0  \n",
+              "min       0.0     0.0     0.0  \n",
+              "25%       0.0     0.0     0.0  \n",
+              "50%       0.0     0.0     0.0  \n",
+              "75%       0.0     0.0     0.0  \n",
+              "max       0.0     0.0     0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "a4uW7nm2mjjI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 313
+        },
+        "outputId": "b0d52828-6080-48ee-ba15-6a83de2d9c2b"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          1       2       3       4       5       6       7       8       9    \\\n",
+              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          10    ...       775     776     777     778     779     780     781  \\\n",
+              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          782     783     784  \n",
+              "count 10000.0 10000.0 10000.0  \n",
+              "mean      0.0     0.0     0.0  \n",
+              "std       0.0     0.0     0.0  \n",
+              "min       0.0     0.0     0.0  \n",
+              "25%       0.0     0.0     0.0  \n",
+              "50%       0.0     0.0     0.0  \n",
+              "75%       0.0     0.0     0.0  \n",
+              "max       0.0     0.0     0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "iRCjWhlQmjjL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "fdb2552a-6844-4d63-90b4-3b6f41229b72"
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_test_input_fn = create_predict_input_fn(\n",
+        "    test_examples, test_targets, batch_size=100)\n",
+        "\n",
+        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+        "  \n",
+        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+        "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.94\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6sfw3LH0Oycm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XatDGFKEO374",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code below is almost identical to the original `LinearClassifer` training code, with the exception of the NN-specific configuration, such as the hyperparameter for hidden units."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kdNTx8jkPQUx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, as well as a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `DNNClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  # Caution: input pipelines are reset with each call to train. \n",
+        "  # If the number of steps is small, your model may never see most of the data.  \n",
+        "  # So with multiple `.train` calls like this you may want to control the length \n",
+        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  # Create a DNNClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.DNNClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      n_classes=10,\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZfzsTYGPPU8I",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_nn_classification_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    hidden_units=[100, 100],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "qXvrOgtUR-zD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we verify the accuracy on the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scQNpDePSFjt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "EVaWpWKvSHmu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_test_input_fn = create_predict_input_fn(\n",
+        "    test_examples, test_targets, batch_size=100)\n",
+        "\n",
+        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+        "  \n",
+        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+        "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "WX2mQBAEcisO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Visualize the weights of the first hidden layer.\n",
+        "\n",
+        "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
+        "\n",
+        "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
+        "\n",
+        "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eUC0Z8nbafgG",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1171
+        },
+        "outputId": "98743231-c0eb-4b31-e11a-a49a7229a3eb"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(classifier.get_variable_names())\n",
+        "\n",
+        "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n",
+        "\n",
+        "print(\"weights0 shape:\", weights0.shape)\n",
+        "\n",
+        "num_nodes = weights0.shape[1]\n",
+        "num_rows = int(math.ceil(num_nodes / 10.0))\n",
+        "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n",
+        "for coef, ax in zip(weights0.T, axes.ravel()):\n",
+        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
+        "    ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n",
+            "weights0 shape: (784, 100)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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3lDUx9YttvtzPmKKXu7lMxUXFEOXsFdisBgo0dTE9DzSmANn7+X2a+j9MNpSV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\nJWhmfURty23U1OgqohcmEy3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t9v11Ki6ZqM7RcfieMn25ttAUcnQNXce4SHCe0WiXlpVE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8zzyDGWu1jPBwOUOy1hFvZrbv8N0vrRx/kgHOeixj8hf0P5FRJyEimXhevWNEpzBOd4YecmEZGe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\nwktVql5U5KWf+y1yxjAMwzAMw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W4lR5tci4hlzhiGYRiGYRiGYRiGYUwo9uOMYRiGYRiG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OgDP6+j9Ak+Xe3/xQ5Pn749431kPj5YXHX7biJ+78k/iOuzv4pLG/gHXd1U9eFnnJ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\n491ahrT1zyH1O/g/cH9cp5ZkSptnl7vQp/QcG+tF+nUgH/G1a1+j6pe/CXM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d+ZCch7/w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\nMYL33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pLBLsRTdqwV0e537MY1OaTHuetEGmvKP0luij/Tkp2slXg2GqXnLJ/jrJi3HvMg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ohg8g4+WzYPXzUnLrG4TkadadJuTEkaS396yUcbDzG8u/0pbKtXeoCXNnsB77p+1aljkT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E+nbFF2kduEN1u2IjN6K8gxZhJcKXzU4mlTAn1EGwtS7U2w2iEHiZoYQe5SCzfNlXlUi1srMYcz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\nwS4AdjtvX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cWDV+CaenE+4PpkjDEjA3LfCCWiaQ8KtkmKxOggzpSF94I2dPCn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ISElJ97Y2PlGeHOwTpNTcUanZiwauNatGMYo9zNbTmMMWakB8/KRppYRQU95AQW6QyL11xe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AsHQkefEE/SLLLCjtOcrl6tFxFNclNBrHGhyh+0RrN65ZyXOomJ9UrCU/3VW4v2H0eSyZ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LSg5TQoowGRoElGWraqQ70WzdaDYLlJd4VcEyxl8o8HNTkkS0pCvP0wv1lyYdvDsuyutQXja1vd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\nC06FHIjXY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7zHaQ8PY84FR8h7MQZU0O4yyH4aT18QURnjkeez7pvutK+8fsRph1tU14R4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\nFAqFQqFQKBQKhWIM8f8AaT+uASLaZJgAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1440x1440 with 100 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kL8MEhNgrx9N",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits.  You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n",
+        "\n",
+        "It can be interesting to stop training at different numbers of iterations and see the effect.\n",
+        "\n",
+        "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n",
+        "\n",
+        "What differences do you see visually for the different levels of convergence?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb
new file mode 100644
index 0000000..7b7459e
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1188 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "sparsity_and_l1_regularization.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "yjUCX5LAkxAX"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/sparsity_and_l1_regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Sparsity and L1 Regularization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "g8ue2FyFIjnQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Calculate the size of a model\n",
+        "  * Apply L1 regularization to reduce the size of a model by increasing sparsity"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ME_WXE7cIjnS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n",
+        "\n",
+        "L1 regularization is a good way to increase sparsity.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fHRzeWkRLrHF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and create feature definitions."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pb7rSrLKIjnS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3V7q8jk0IjnW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pAG3tmgwIjnY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1153
+        },
+        "outputId": "7d101829-c3f7-432c-f48a-58392736ec3e"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2659.1           541.9   \n",
+              "std         2.1        2.0                12.6       2204.5           425.5   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1467.8           297.0   \n",
+              "50%        34.3     -118.5                29.0       2140.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3159.0           651.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1434.4       502.8            3.9               2.0  \n",
+              "std        1170.6       388.0            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         795.0       282.0            2.6               1.5  \n",
+              "50%        1166.0       409.0            3.6               1.9  \n",
+              "75%        1720.2       607.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2659.1</td>\n",
+              "      <td>541.9</td>\n",
+              "      <td>1434.4</td>\n",
+              "      <td>502.8</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2204.5</td>\n",
+              "      <td>425.5</td>\n",
+              "      <td>1170.6</td>\n",
+              "      <td>388.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1467.8</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>795.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2140.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1166.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3159.0</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1720.2</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2606.5           533.4   \n",
+              "std         2.1        2.0                12.6       2119.7           411.7   \n",
+              "min        32.5     -124.3                 2.0         15.0             4.0   \n",
+              "25%        33.9     -121.7                18.0       1447.0           295.0   \n",
+              "50%        34.2     -118.5                29.0       2105.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3133.5           644.2   \n",
+              "max        42.0     -114.6                52.0      30405.0          4957.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1418.1       497.4            3.9               2.0  \n",
+              "std        1091.5       375.9            2.0               1.3  \n",
+              "min          15.0         5.0            0.5               0.3  \n",
+              "25%         778.0       279.0            2.6               1.5  \n",
+              "50%        1168.0       410.0            3.5               1.9  \n",
+              "75%        1721.0       602.0            4.7               2.3  \n",
+              "max       13251.0      4490.0           15.0              52.0  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2606.5</td>\n",
+              "      <td>533.4</td>\n",
+              "      <td>1418.1</td>\n",
+              "      <td>497.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2119.7</td>\n",
+              "      <td>411.7</td>\n",
+              "      <td>1091.5</td>\n",
+              "      <td>375.9</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.7</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1447.0</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>778.0</td>\n",
+              "      <td>279.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2105.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3133.5</td>\n",
+              "      <td>644.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>602.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30405.0</td>\n",
+              "      <td>4957.0</td>\n",
+              "      <td>13251.0</td>\n",
+              "      <td>4490.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gHkniRI1Ijna",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                            \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bLzK72jkNJPf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_buckets(feature_values, num_buckets):\n",
+        "  quantiles = feature_values.quantile(\n",
+        "    [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n",
+        "  return [quantiles[q] for q in quantiles.keys()]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "al2YQpKyIjnd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"households\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"longitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"latitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"housing_median_age\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"housing_median_age\"], 10))\n",
+        "  bucketized_total_rooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_rooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n",
+        "  bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_bedrooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n",
+        "  bucketized_population = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"population\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"median_income\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"rooms_per_person\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"rooms_per_person\"], 10))\n",
+        "\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "    set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n",
+        "\n",
+        "  feature_columns = set([\n",
+        "    long_x_lat,\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_total_rooms,\n",
+        "    bucketized_total_bedrooms,\n",
+        "    bucketized_population,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hSBwMrsrE21n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Calculate the Model Size\n",
+        "\n",
+        "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "e6GfTI0CFhB8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def model_size(estimator):\n",
+        "  variables = estimator.get_variable_names()\n",
+        "  size = 0\n",
+        "  for variable in variables:\n",
+        "    if not any(x in variable \n",
+        "               for x in ['global_step',\n",
+        "                         'centered_bias_weight',\n",
+        "                         'bias_weight',\n",
+        "                         'Ftrl']\n",
+        "              ):\n",
+        "      size += np.count_nonzero(estimator.get_variable_value(variable))\n",
+        "  return size"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XabdAaj67GfF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Reduce the Model Size\n",
+        "\n",
+        "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n",
+        "\n",
+        "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n",
+        "\n",
+        "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G79hGRe7qqej",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1: Find a good regularization coefficient.\n",
+        "\n",
+        "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n",
+        "\n",
+        "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n",
+        "\n",
+        "Again, the model will train on the entire data set, so expect it to run slower than normal."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1Fcdm0hpIjnl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    regularization_strength,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    regularization_strength: A `float` that indicates the strength of the L1\n",
+        "       regularization. A value of `0.0` means no regularization.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 7\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\n",
+        "  \n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"LogLoss (on validation data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9H1CKHSzIjno",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 586
+        },
+        "outputId": "b1b2617a-72ad-44c1-aedb-dee1e1514270"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    # TWEAK THE REGULARIZATION VALUE BELOW\n",
+        "    regularization_strength=0.0,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\n",
+            "  period 01 : 0.28\n",
+            "  period 02 : 0.27\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.25\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.25\n",
+            "Model training finished.\n",
+            "Model size: 784\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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mCoUQQgjbIGHGho0a2JWwrp78lF7A4ZNFrR7HWevEnX0ap5s+Pi7TTUIIIToW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\npf1/ldi97fAh42wAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yjUCX5LAkxAX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hgGhy-okmkWL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n",
+        "stronger regularization gives us smaller models, but can affect the classification loss."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_rV8YQWZIjns",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 586
+        },
+        "outputId": "cb97892c-8ba0-4df4-c6de-1f3c66aa8c3a"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    regularization_strength=0.1,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\n",
+            "  period 01 : 0.28\n",
+            "  period 02 : 0.27\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.26\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.25\n",
+            "Model training finished.\n",
+            "Model size: 750\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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aGRuk0Vy994yiwLKtp6nXG0wb5/9XNzlqHVl9ej1ltabt0C2EEELYMmlmbFRI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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rHNPYhfnYBM7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb
new file mode 100644
index 0000000..6e77d8e
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1202 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "synthetic_features_and_outliers.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "i5Ul3zf5QYvW",
+        "jByCP8hDRZmM",
+        "WvgxW0bUSC-c"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/synthetic_features_and_outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Synthetic Features and Outliers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jnKgkN5fHbGy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Create a synthetic feature that is the ratio of two other features\n",
+        "  * Use this new feature as an input to a linear regression model\n",
+        "  * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VOpLo5dcHbG0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n",
+        "\n",
+        "First, we'll import the California housing data into a *pandas* `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S8gm6BpqRRuh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9D8GgUovHbG0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 402
+        },
+        "outputId": "337343cb-da97-465f-817e-8e67c7d9bb31"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import sklearn.metrics as metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16704</th>\n",
+              "      <td>-122.8</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>1318.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>567.0</td>\n",
+              "      <td>276.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>93.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9011</th>\n",
+              "      <td>-119.0</td>\n",
+              "      <td>35.3</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>3480.0</td>\n",
+              "      <td>608.0</td>\n",
+              "      <td>2007.0</td>\n",
+              "      <td>541.0</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>78.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5222</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>34.7</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>2181.0</td>\n",
+              "      <td>361.0</td>\n",
+              "      <td>1057.0</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>118.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14672</th>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>1985.0</td>\n",
+              "      <td>440.0</td>\n",
+              "      <td>1085.0</td>\n",
+              "      <td>407.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>136.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8675</th>\n",
+              "      <td>-118.6</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>1962.0</td>\n",
+              "      <td>243.0</td>\n",
+              "      <td>697.0</td>\n",
+              "      <td>242.0</td>\n",
+              "      <td>8.6</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4107</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>33.8</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>1290.0</td>\n",
+              "      <td>220.0</td>\n",
+              "      <td>867.0</td>\n",
+              "      <td>241.0</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>218.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3563</th>\n",
+              "      <td>-117.9</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>3250.0</td>\n",
+              "      <td>521.0</td>\n",
+              "      <td>1382.0</td>\n",
+              "      <td>513.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>218.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11031</th>\n",
+              "      <td>-121.0</td>\n",
+              "      <td>39.1</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>2069.0</td>\n",
+              "      <td>436.0</td>\n",
+              "      <td>909.0</td>\n",
+              "      <td>374.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>139.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9252</th>\n",
+              "      <td>-119.1</td>\n",
+              "      <td>36.4</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>3146.0</td>\n",
+              "      <td>595.0</td>\n",
+              "      <td>1580.0</td>\n",
+              "      <td>513.0</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>92.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16663</th>\n",
+              "      <td>-122.8</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>2895.0</td>\n",
+              "      <td>524.0</td>\n",
+              "      <td>1633.0</td>\n",
+              "      <td>534.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>170.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "16704     -122.8      39.0                15.0       1318.0           296.0   \n",
+              "9011      -119.0      35.3                29.0       3480.0           608.0   \n",
+              "5222      -118.1      34.7                15.0       2181.0           361.0   \n",
+              "14672     -122.2      37.8                43.0       1985.0           440.0   \n",
+              "8675      -118.6      34.2                31.0       1962.0           243.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "4107      -118.0      33.8                34.0       1290.0           220.0   \n",
+              "3563      -117.9      34.1                29.0       3250.0           521.0   \n",
+              "11031     -121.0      39.1                24.0       2069.0           436.0   \n",
+              "9252      -119.1      36.4                21.0       3146.0           595.0   \n",
+              "16663     -122.8      38.4                11.0       2895.0           524.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "16704       567.0       276.0            1.9                93.8  \n",
+              "9011       2007.0       541.0            3.3                78.7  \n",
+              "5222       1057.0       300.0            4.6               118.1  \n",
+              "14672      1085.0       407.0            3.4               136.7  \n",
+              "8675        697.0       242.0            8.6               500.0  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "4107        867.0       241.0            5.5               218.1  \n",
+              "3563       1382.0       513.0            5.1               218.3  \n",
+              "11031       909.0       374.0            2.5               139.1  \n",
+              "9252       1580.0       513.0            2.8                92.7  \n",
+              "16663      1633.0       534.0            4.7               170.2  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6kNgrwCO_ms",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll set up our input function, and define the function for model training:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5RpTJER9XDub",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VgQPftrpHbG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
+        "    after training the model.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
+        "\n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "    \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "      metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "    \n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Create a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
+        "  \n",
+        "  return calibration_data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FJ6xUNVRm-do",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Try a Synthetic Feature\n",
+        "\n",
+        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
+        "\n",
+        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
+        "\n",
+        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
+        "\n",
+        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
+        "the final RMSE should be.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "isONN2XK32Wo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5ihcVutnnu1D",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 939
+        },
+        "outputId": "327dfcf1-9993-406d-fae2-cb0f48290d11"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.78\n",
+            "  period 01 : 189.68\n",
+            "  period 02 : 169.73\n",
+            "  period 03 : 152.85\n",
+            "  period 04 : 141.91\n",
+            "  period 05 : 134.48\n",
+            "  period 06 : 131.45\n",
+            "  period 07 : 130.98\n",
+            "  period 08 : 130.87\n",
+            "  period 09 : 131.75\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         194.0    207.3\n",
+              "std           89.6    116.0\n",
+              "min           43.6     15.0\n",
+              "25%          158.9    119.4\n",
+              "50%          191.0    180.4\n",
+              "75%          218.3    265.0\n",
+              "max         4275.5    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>194.0</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>89.6</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>43.6</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>158.9</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>191.0</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>218.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4275.5</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 131.75\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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GPFcoLy/nrrvucjZABEhKSuLAgQP07duXXr16ER8fT25uLna7nRUrVjj/Ljg42NkgMTk5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vO8Az72xmRP9EZkxJxx3rDHd5Ip2W7mCbrVlfxf8sKCQ+1sYjP+t3zLSgRwSDBu/86xBv/bMY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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i5Ul3zf5QYvW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Leaz2oYMQcBf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZjQrZ8mcHFiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Identify Outliers\n",
+        "\n",
+        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
+        "\n",
+        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
+        "\n",
+        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P0BDOec4HbG_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390
+        },
+        "outputId": "eb3c701e-d433-4827-9865-84ecae0e94ee"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fd6a6ddd610>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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zz4p0OiCcREdYz4Aq3HyWQlqSbyWpSBs0HYjEoIRDpzLXpTWZIATI7xHisxTS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\nAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jByCP8hDRZmM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s0tiX2gdRe-S",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390
+        },
+        "outputId": "0fe56966-9773-4fbc-8bfe-f73a2f2d8aa6"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fd6a6a60a50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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zz4p0OiCcREdYz4Aq3HyWQlqSbyWpSBs0HYjEoIRDpzLXpTWZIATI7xHisxTS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\nAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kMQD0Uq3RqTX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
+        "\n",
+        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "POTM8C_ER1Oc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "6cdf09ec-ccac-4e7c-9cd9-39ba2e36db47"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "88f164ed-6d8b-4a0d-ba0f-acfaa50bf3e8"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Y-OgJgRvHvdq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 939
+        },
+        "outputId": "a8eb7701-b3fb-4582-e473-c1317ccf8662"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.80\n",
+            "  period 01 : 189.03\n",
+            "  period 02 : 166.67\n",
+            "  period 03 : 146.50\n",
+            "  period 04 : 129.82\n",
+            "  period 05 : 118.85\n",
+            "  period 06 : 113.05\n",
+            "  period 07 : 110.47\n",
+            "  period 08 : 109.74\n",
+            "  period 09 : 109.09\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         188.7    207.3\n",
+              "std           49.8    116.0\n",
+              "min           42.6     15.0\n",
+              "25%          156.9    119.4\n",
+              "50%          188.8    180.4\n",
+              "75%          215.9    265.0\n",
+              "max          421.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>188.7</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>49.8</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>42.6</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>156.9</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>188.8</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>215.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>421.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 109.09\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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s59012RQUV5odkoiIiGmUlBA5DWhYTRGRwBITGcy00d2pcHp4c9m3GGrGISIi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PqV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\nlAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "aDsuABBSHvd2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "ec68fdf7-211c-4ac6-e102-e1b65f9efa56"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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uHlU5R0avx3OPrlHVh6wGtRtrLRtws9GAP/mD62ZdYZPcqMxScVJmKzPeIAMT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YMWWi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\nTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WvgxW0bUSC-c",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8YGNjXPaSMPV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9YyARz6gSR7Q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vO0e1p_aSgKA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To verify that clipping worked, let's train again and print the calibration data once more:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZgSP2HKfSoOH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "gySE-UgfSony",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/validation.ipynb b/validation.ipynb
new file mode 100644
index 0000000..d1d5519
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1535 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "validation.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "4Xp9NhOCYSuz",
+        "pECTKgw5ZvFK",
+        "dER2_43pWj1T",
+        "I-La4N9ObC1x",
+        "yTghc_5HkJDW"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Anubhavkumar45/Assignment-5/blob/Anubhavkumar45/validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zbIgBK-oXHO7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Validation"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WNX0VyBpHpCX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Use multiple features, instead of a single feature, to further improve the effectiveness of a model\n",
+        "  * Debug issues in model input data\n",
+        "  * Use a test data set to check if a model is overfitting the validation data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "za0m1T8CHpCY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), to try and predict `median_house_value` at the city block level from 1990 census data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r2zgMfWDWF12",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8jErhkLzWI1B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First off, let's load up and prepare our data. This time, we're going to work with multiple features, so we'll modularize the logic for preprocessing the features a bit:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PwS5Bhm6HpCZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "# california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "#     np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "J2ZyTzX0HpCc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "sZSIaDiaHpCf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **training set**, we'll choose the first 12000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P9wejvw7HpCf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "fd60dcde-300a-42d9-a02d-ef802d2a372e"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>34.6</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>2655.7</td>\n",
+              "      <td>547.1</td>\n",
+              "      <td>1476.0</td>\n",
+              "      <td>505.4</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>12.1</td>\n",
+              "      <td>2258.1</td>\n",
+              "      <td>434.3</td>\n",
+              "      <td>1174.3</td>\n",
+              "      <td>391.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.8</td>\n",
+              "      <td>-118.9</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1451.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>815.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.0</td>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2113.5</td>\n",
+              "      <td>438.0</td>\n",
+              "      <td>1207.0</td>\n",
+              "      <td>411.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>34.4</td>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>3146.0</td>\n",
+              "      <td>653.0</td>\n",
+              "      <td>1777.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.8</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       34.6     -118.5                27.5       2655.7           547.1   \n",
+              "std         1.6        1.2                12.1       2258.1           434.3   \n",
+              "min        32.5     -121.4                 1.0          2.0             2.0   \n",
+              "25%        33.8     -118.9                17.0       1451.8           299.0   \n",
+              "50%        34.0     -118.2                28.0       2113.5           438.0   \n",
+              "75%        34.4     -117.8                36.0       3146.0           653.0   \n",
+              "max        41.8     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1476.0       505.4            3.8               1.9  \n",
+              "std        1174.3       391.7            1.9               1.3  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         815.0       283.0            2.5               1.4  \n",
+              "50%        1207.0       411.0            3.5               1.9  \n",
+              "75%        1777.0       606.0            4.6               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "a751136b-41fc-4291-e331-e9cbb402425b"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>198.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>111.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>117.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>170.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>244.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                198.0\n",
+              "std                 111.9\n",
+              "min                  15.0\n",
+              "25%                 117.1\n",
+              "50%                 170.5\n",
+              "75%                 244.4\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5l1aA2xOHpCj",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fLYXLWAiHpCk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "bddc1db6-39c9-4122-81ea-017f7f5f1652"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>38.1</td>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>31.3</td>\n",
+              "      <td>2614.8</td>\n",
+              "      <td>521.1</td>\n",
+              "      <td>1318.1</td>\n",
+              "      <td>491.2</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>13.4</td>\n",
+              "      <td>1979.6</td>\n",
+              "      <td>388.5</td>\n",
+              "      <td>1073.7</td>\n",
+              "      <td>366.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>36.1</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>37.5</td>\n",
+              "      <td>-122.4</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1481.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>731.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>37.8</td>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>2164.0</td>\n",
+              "      <td>424.0</td>\n",
+              "      <td>1074.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>3161.2</td>\n",
+              "      <td>635.0</td>\n",
+              "      <td>1590.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>2.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>18.3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       38.1     -122.2                31.3       2614.8           521.1   \n",
+              "std         0.9        0.5                13.4       1979.6           388.5   \n",
+              "min        36.1     -124.3                 1.0          8.0             1.0   \n",
+              "25%        37.5     -122.4                20.0       1481.0           292.0   \n",
+              "50%        37.8     -122.1                31.0       2164.0           424.0   \n",
+              "75%        38.4     -121.9                42.0       3161.2           635.0   \n",
+              "max        42.0     -121.4                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1318.1       491.2            4.1               2.1  \n",
+              "std        1073.7       366.5            2.0               0.6  \n",
+              "min           8.0         1.0            0.5               0.1  \n",
+              "25%         731.0       278.0            2.7               1.7  \n",
+              "50%        1074.0       403.0            3.7               2.1  \n",
+              "75%        1590.2       603.0            5.1               2.4  \n",
+              "max       28566.0      6082.0           15.0              18.3  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "72aee58b-339b-490c-8ec5-c42080b888ab"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>229.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>122.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>130.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>213.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>303.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                229.5\n",
+              "std                 122.5\n",
+              "min                  15.0\n",
+              "25%                 130.4\n",
+              "50%                 213.0\n",
+              "75%                 303.2\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z3TZV1pgfZ1n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Examine the Data\n",
+        "Okay, let's look at the data above. We have `9` input features that we can use.\n",
+        "\n",
+        "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense  will get you far.\n",
+        "\n",
+        "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4Xp9NhOCYSuz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gqeRmK57YWpy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's check our data against some baseline expectations:\n",
+        "\n",
+        "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n",
+        "\n",
+        "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n",
+        "\n",
+        "If you look closely, you may see some oddities:\n",
+        "\n",
+        "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n",
+        "\n",
+        "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n",
+        "\n",
+        "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n",
+        "\n",
+        "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fXliy7FYZZRm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Plot Latitude/Longitude vs. Median House Value"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aJIWKBdfsDjg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n",
+        "\n",
+        "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5_LD23bJ06TW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "outputId": "bee21df8-adda-41a7-b754-68a0045537b6"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(13, 8))\n",
+        "\n",
+        "ax = plt.subplot(1, 2, 1)\n",
+        "ax.set_title(\"Validation Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(validation_examples[\"longitude\"],\n",
+        "            validation_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
+        "\n",
+        "ax = plt.subplot(1,2,2)\n",
+        "ax.set_title(\"Training Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(training_examples[\"longitude\"],\n",
+        "            training_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
+        "_ = plt.plot()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M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\nYII=\n",
+            "text/plain": [
+              "<Figure size 936x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "32_DbjnfXJlC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n",
+        "\n",
+        "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n",
+        "\n",
+        "**Go back up and look at the data from Task 1 again.**\n",
+        "\n",
+        "Do you see any other differences in the distributions of features or targets between the training and validation data?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pECTKgw5ZvFK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "49NC4_KIZxk_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75<sup>th</sup> percentile value for total_rooms per city block?\n",
+        "\n",
+        "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n",
+        "\n",
+        "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "025Ky0Dq9ig0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3:  Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n",
+        "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JFsd2eWHAMdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n",
+        "\n",
+        "By the way, there's an important lesson here.\n",
+        "\n",
+        "**Debugging in ML is often *data debugging* rather than code debugging.**\n",
+        "\n",
+        "If the data is wrong, even the most advanced ML code can't save things."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dER2_43pWj1T",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BnEVbYJvW2wu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n",
+        "\n",
+        "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xCdqLpQyAos2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 4: Train and Evaluate a Model\n",
+        "\n",
+        "**Spend 5 minutes or so trying different hyperparameter settings.  Try to get the best validation performance you can.**\n",
+        "\n",
+        "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n",
+        "\n",
+        "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rzcIPGxxgG0t",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "CvrKoBmNgRCO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wEW5_XYtgZ-H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D0o2wnnzf8BD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n",
+        "\n",
+        "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n",
+        "\n",
+        "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n",
+        "\n",
+        "See how much better you can do now that we can use multiple features.\n",
+        "\n",
+        "Check the data using some of the methods we've looked at before.  These might include:\n",
+        "\n",
+        "   * Comparing distributions of predictions and actual target values\n",
+        "\n",
+        "   * Creating a scatter plot of predictions vs. target values\n",
+        "\n",
+        "   * Creating two scatter plots of validation data using `latitude` and `longitude`:\n",
+        "      * One plot mapping color to actual target `median_house_value`\n",
+        "      * A second plot mapping color to predicted `median_house_value` for side-by-side comparison."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UXt0_4ZTEf4V",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(\n",
+        "      validation_examples, validation_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zFFRmvUGh8wd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "outputId": "52660a35-4d30-488e-c2f0-62d0ea80309b"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 226.04\n",
+            "  period 01 : 224.77\n",
+            "  period 02 : 223.43\n",
+            "  period 03 : 222.05\n",
+            "  period 04 : 220.70\n",
+            "  period 05 : 219.36\n",
+            "  period 06 : 218.01\n",
+            "  period 07 : 216.69\n",
+            "  period 08 : 215.40\n",
+            "  period 09 : 214.08\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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yvYpzU/UpUaEm1FuTJjqhU2s7860QEcUcAwxRnDtR4XBtYy2K/KXyasORnpuW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\n0HT45AAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I-La4N9ObC1x",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Xyz6n1YHbGef",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(\n",
+        "      validation_examples, validation_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "i1imhjFzbWwt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    learning_rate=0.00003,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "65sin-E5NmHN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 5: Evaluate on Test Data\n",
+        "\n",
+        "**In the cell below, load in the test data set and evaluate your model on it.**\n",
+        "\n",
+        "We've done a lot of iteration on our validation data.  Let's make sure we haven't overfit to the pecularities of that particular sample.\n",
+        "\n",
+        "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n",
+        "\n",
+        "How does your test performance compare to the validation performance?  What does this say about the generalization performance of your model?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "c72ed904-800e-443b-dc9f-42a2037c12ed"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_test_input_fn = lambda: my_input_fn(\n",
+        "      test_examples, \n",
+        "      test_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 221.60\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yTghc_5HkJDW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_xSYTarykO8U",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_test_input_fn = lambda: my_input_fn(\n",
+        "      test_examples, \n",
+        "      test_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file